Preprints of Publications and Submissions

Mykola Pechenizkiy

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journal Du, X., Sun, L., Duivesteijn, W., Nikolaev, A. & Pechenizkiy, M. (2021) Adversarial balancing-based representation learning for causal effect inference with observational data, Data Mining and Knowledge Discovery.

BibTeX: 
@article{Du2021a,
  author = {Du, Xin and Sun, Lei and Duivesteijn, Wouter and Nikolaev, Alexander and Pechenizkiy, Mykola},
  title = {Adversarial balancing-based representation learning for causal effect inference with observational data},
  journal = {Data Mining and Knowledge Discovery},
  year = {2021},
  url = {https://doi.org/10.1007/s10618-021-00759-3},
  doi = {http://doi.org/10.1007/s10618-021-00759-3}
}
preprint Huang, T., Menkovski, V., Pei, Y., Wang, Y. & Pechenizkiy, M. (2021) Direction-Aggregated Attack for Transferable Adversarial Examples.

BibTeX: 
@techreport{Huang2021,
  author = {Tianjin Huang and Vlado Menkovski and Yulong Pei and Yuhao Wang and Mykola Pechenizkiy},
  title = {Direction-Aggregated Attack for Transferable Adversarial Examples},
  journal = {arXiv},
  year = {2021},
  volume = {abs/2104.09172},
  url = {https://arxiv.org/abs/2104.09172},
  file = {Huang2021.pdf:https//arxiv.org/abs/2104.09172.pdf:PDF}
}
preprint Liu, S., Yin, L., Mocanu, D.C. & Pechenizkiy, M. (2021) Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training, In Proceedings of International Conference on Machine Learning (ICML 2021)

BibTeX: 
@inproceedings{Liu2021b,
  author = {Shiwei Liu and Lu Yin and Decebal Constantin Mocanu and Mykola Pechenizkiy},
  title = {Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training},
  booktitle = {Proceedings of International Conference on Machine Learning (ICML 2021)},
  year = {2021},
  volume = {abs/2102.02887},
  url = {https://arxiv.org/abs/2102.02887},
  file = {Liu2021b.pdf:https//arxiv.org/abs/2102.02887.pdf:PDF}
}
journal Liu, S., Ni’mah, I., Menkovski, V., Mocanu, D.C. & Pechenizkiy, M. (2021) Efficient and effective training of sparse recurrent neural networks, Neural Computing and Applications.

BibTeX: 
@article{Liu2021c,
  author = {Liu, Shiwei and Ni’mah, Iftitahu and Menkovski, Vlado and Mocanu, Decebal Constantin and Pechenizkiy, Mykola},
  title = {Efficient and effective training of sparse recurrent neural networks},
  journal = {Neural Computing and Applications},
  year = {2021},
  url = {https://doi.org/10.1007/s00521-021-05727-y},
  doi = {http://doi.org/10.1007/s00521-021-05727-y}
}
conference Halstead, B., Koh, Y.S., Riddle, P., Pechenizkiy, M., Bifet, A. & Pears, R. (2021) Fingeprinting concepts in data streams with supervised and unsupervised meta-information, In Proceedings of IEEE International Conference on Data Engineering (ICDE 2021).

BibTeX: 
@inproceedings{Halstead2021a,
  author = {Halstead, Ben and Koh, Yun Sing and Riddle, Patricia and Pechenizkiy, Mykola and Bifet, Albert and Pears, Russel},
  title = {Fingeprinting concepts in data streams with supervised and unsupervised meta-information},
  booktitle = {Proceedings of IEEE International Conference on Data Engineering (ICDE 2021)},
  year = {2021}
}
preprint Huang, T., Pei, Y., Menkovski, V. & Pechenizkiy, M. (2021) Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks.

BibTeX: 
@techreport{Huang2021a,
  author = {Tianjin Huang and Yulong Pei and Vlado Menkovski and Mykola Pechenizkiy},
  title = {Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks},
  journal = {arXiv},
  year = {2021},
  volume = {abs/2104.07917},
  url = {https://arxiv.org/abs/2104.07917},
  file = {Huang2021a.pdf:https//arxiv.org/abs/2104.07917.pdf:PDF}
}
preprint Saxena, A., Fletcher, G. & Pechenizkiy, M. (2021) How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking.

BibTeX: 
@techreport{Saxena2021a,
  author = {Akrati Saxena and George Fletcher and Mykola Pechenizkiy},
  title = {How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking},
  journal = {arXiv},
  year = {2021},
  volume = {abs/2103.01335},
  url = {https://arxiv.org/abs/2103.01335},
  file = {Saxena2021a.pdf:https//arxiv.org/abs/2103.01335.pdf:PDF}
}
workshop Sokar, G., Mocanu, D.C. & Pechenizkiy, M. (2021) Learning Invariant Representation for Continual Learning, In Proceedings of International Workshops on Meta-Learning for Computer Vision (MeL4CV) at AAAI 2021, abs/2101.06162.

BibTeX: 
@inproceedings{Sokar2021c,
  author = {Ghada Sokar and Decebal Constantin Mocanu and Mykola Pechenizkiy},
  title = {Learning Invariant Representation for Continual Learning},
  booktitle = {Proceedings of International Workshops on Meta-Learning for Computer Vision (MeL4CV) at AAAI 2021},
  year = {2021},
  volume = {abs/2101.06162},
  url = {https://arxiv.org/abs/2101.06162},
  file = {Sokar2021c.pdf:https//arxiv.org/abs/2101.06162.pdf:PDF}
}
preprint Saxena, A., Fletcher, G. & Pechenizkiy, M. (2021) NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction.

BibTeX: 
@techreport{Saxena2021b,
  author = {Akrati Saxena and George Fletcher and Mykola Pechenizkiy},
  title = {NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction},
  journal = {arXiv},
  year = {2021},
  volume = {abs/2102.00785},
  url = {https://arxiv.org/abs/2102.00785},
  file = {Saxena2021b.pdf:https//arxiv.org/abs/2102.00785.pdf:PDF}
}
journal Halstead, B., Koh, Y.S., Riddle, P., Pears, R., Pechenizkiy, M. & Bifet, A. (2021) Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency, Data Mining and Knowledge Discovery, 35, pp. 796-836.

BibTeX: 
@article{Halstead2021,
  author = {Halstead, Ben and Koh, Yun Sing and Riddle, Patricia and Pears, Russel and Pechenizkiy, Mykola and Bifet, Albert},
  title = {Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency},
  journal = {Data Mining and Knowledge Discovery},
  year = {2021},
  volume = {35},
  pages = {796--836},
  url = {https://doi.org/10.1007/s10618-021-00736-w},
  doi = {http://doi.org/10.1007/s10618-021-00736-w}
}
preprint Sokar, G., Mocanu, D.C. & Pechenizkiy, M. (2021) Self-Attention Meta-Learner for Continual Learning.

BibTeX: 
@techreport{Sokar2021b,
  author = {Ghada Sokar and Decebal Constantin Mocanu and Mykola Pechenizkiy},
  title = {Self-Attention Meta-Learner for Continual Learning},
  journal = {arXiv},
  year = {2021},
  volume = {abs/2101.12136},
  url = {https://arxiv.org/abs/2101.12136},
  file = {Sokar2021b.pdf:https//arxiv.org/abs/2101.12136.pdf:PDF}
}
preprint Liu, S., Mocanu, D.C., Pei, Y. & Pechenizkiy, M. (2021) Selfish Sparse RNN Training, In Proceedings of International Conference on Machine Learning (ICML 2021)

BibTeX: 
@inproceedings{Liu2021d,
  author = {Shiwei Liu and Decebal Constantin Mocanu and Yulong Pei and Mykola Pechenizkiy},
  title = {Selfish Sparse RNN Training},
  booktitle = {Proceedings of International Conference on Machine Learning (ICML 2021)},
  year = {2021},
  volume = {abs/2101.09048},
  url = {https://arxiv.org/abs/2101.09048},
  file = {Liu2021d.pdf:https//arxiv.org/abs/2101.09048.pdf:PDF}
}
journal Sokar, G., Mocanu, D.C. & Pechenizkiy, M. (2021) SpaceNet: Make Free Space for Continual Learning, Neurocomputing, 439, pp. 1-11.

Abstract: The continual learning (CL) paradigm aims to enable neural networks to learn tasks continually in a sequential fashion. The fundamental challenge in this learning paradigm is catastrophic forgetting previously learned tasks when the model is optimized for a new task, especially when their data is not accessible. Current architectural-based methods aim at alleviating the catastrophic forgetting problem but at the expense of expanding the capacity of the model. Regularization-based methods maintain a fixed model capacity; however, previous studies showed the huge performance degradation of these methods when the task identity is not available during inference (e.g. class incremental learning scenario). In this work, we propose a novel architectural-based method referred as SpaceNet11Code available at: https://github.com/GhadaSokar/SpaceNet for class incremental learning scenario where we utilize the available fixed capacity of the model intelligently. SpaceNet trains sparse deep neural networks from scratch in an adaptive way that compresses the sparse connections of each task in a compact number of neurons. The adaptive training of the sparse connections results in sparse representations that reduce the interference between the tasks. Experimental results show the robustness of our proposed method against catastrophic forgetting old tasks and the efficiency of SpaceNet in utilizing the available capacity of the model, leaving space for more tasks to be learned. In particular, when SpaceNet is tested on the well-known benchmarks for CL: split MNIST, split Fashion-MNIST, CIFAR-10/100, and iCIFAR100, it outperforms regularization-based methods by a big performance gap. Moreover, it achieves better performance than architectural-based methods without model expansion and achieves comparable results with rehearsal-based methods, while offering a huge memory reduction.
BibTeX: 
@article{Sokar2021a,
  author = {Ghada Sokar and Decebal Constantin Mocanu and Mykola Pechenizkiy},
  title = {SpaceNet: Make Free Space for Continual Learning},
  journal = {Neurocomputing},
  year = {2021},
  volume = {439},
  pages = {1-11},
  url = {https://www.sciencedirect.com/science/article/pii/S0925231221001545},
  doi = {http://doi.org/10.1016/j.neucom.2021.01.078}
}
journal Liu, S., Mocanu, D.C., Matavalam, A.R.R., Pei, Y. & Pechenizkiy, M. (2021) Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware, Neural Computing and Applications, 33, pp. 2589-2604.

BibTeX: 
@article{Liu2021a,
  author = {Liu, Shiwei and Mocanu, Decebal Constantin and Matavalam, Amarsagar Reddy Ramapuram and Pei, Yulong and Pechenizkiy, Mykola},
  title = {Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware},
  journal = {Neural Computing and Applications},
  year = {2021},
  volume = {33},
  pages = {2589--2604},
  url = {https://doi.org/10.1007/s00521-020-05136-7},
  doi = {http://doi.org/10.1007/s00521-020-05136-7}
}
preprint Curci, S., Mocanu, D.C. & Pechenizkiy, M. (2021) Truly Sparse Neural Networks at Scale.

BibTeX: 
@techreport{Curci2021,
  author = {Selima Curci and Decebal Constantin Mocanu and Mykola Pechenizkiy},
  title = {Truly Sparse Neural Networks at Scale},
  journal = {arXiv},
  year = {2021},
  volume = {abs/2102.01732},
  url = {https://arxiv.org/abs/2102.01732},
  file = {Curci2021.pdf:https//arxiv.org/abs/2102.01732.pdf:PDF}
}
preprint Ravi, S., Khoshrou, S. & Pechenizkiy, M. (2020) ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic.

BibTeX: 
@techreport{ravi2020,
  author = {Sahithya Ravi and Samaneh Khoshrou and Mykola Pechenizkiy},
  title = {ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic},
  journal = {arXiv},
  year = {2020},
  file = {Ravi2020.pdf:https//arxiv.org/pdf/2011.14871v1:PDF}
}
journal Du, X., Pei, Y., Duivesteijn, W. & Pechenizkiy, M. (2020) Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling, Data Mining and Knowledge Discovery, Springer Open.

Abstract: Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior.
BibTeX: 
@article{Du2020,
  author = {Xin Du and Yulong Pei and Wouter Duivesteijn and Mykola Pechenizkiy},
  title = {Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling},
  journal = {Data Mining and Knowledge Discovery},
  publisher = {Springer Open},
  year = {2020},
  doi = {http://doi.org/10.1007/s10618-020-00674-z},
  file = {Du2020.pdf:https//link.springer.com/content/pdf/10.1007/s10618-020-00674-z.pdf:PDF}
}
journal Pei, Y., Du, X., Zhang, J., Fletcher, G. & Pechenizkiy, M. (2020) struc2gauss: Structural role preserving network embedding via Gaussian embedding, Data Mining and Knowledge Discovery, Springer Open.

Abstract: Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.
BibTeX: 
@article{Pei2020,
  author = {Yulong Pei and Xin Du and Jianpeng Zhang and George Fletcher and Mykola Pechenizkiy},
  title = {struc2gauss: Structural role preserving network embedding via Gaussian embedding},
  journal = {Data Mining and Knowledge Discovery},
  publisher = {Springer Open},
  year = {2020},
  doi = {http://doi.org/10.1007/s10618-020-00684-x},
  file = {Pei2020.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Pei2020.pdf:PDF}
}
conference Du, X., Pei, Y., Duivesteijn, W. & Pechenizkiy, M. (2020) Fairness in network representation by latent structural heterogeneity in observational data, In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), AAAI Press, pp. 3809-3816.

BibTeX: 
@inproceedings{Du2020a,
  author = {Xin Du and Yulong Pei and Wouter Duivesteijn and Mykola Pechenizkiy},
  title = {Fairness in network representation by latent structural heterogeneity in observational data},
  booktitle = {Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)},
  publisher = {AAAI Press},
  year = {2020},
  pages = {3809--3816},
  file = {Du2020a.pdf:http//wwwis.win.tue.nl/ wouter/Publ/C21-MLSD.pdf:PDF}
}
conference Yin, L., Menkovski, V. & Pechenizkiy, M. (2020) Knowledge Elicitation using Deep Metric Learning and Psychometric Testing, In Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Research Track.

BibTeX: 
@inproceedings{yin_ECMLPKDD2020,
  author = {Lu Yin and Vlado Menkovski and Mykola Pechenizkiy},
  title = {Knowledge Elicitation using Deep Metric Learning and Psychometric Testing},
  booktitle = {Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Research Track},
  year = {2020},
  file = {yin_ECMLPKDD2020.pdf:https//arxiv.org/pdf/2004.06353.pdf:PDF}
}
conference Fajri, R., Khoshrou, S., Peharz, R. & Pechenizkiy, M. (2020) PS3: Partition-based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data, In Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Applied Data Science Track.

BibTeX: 
@inproceedings{Fajri_ECMLPKDD2020rohtua,
  author = {Ricky Fajri and Samaneh Khoshrou and Robert Peharz and Mykola Pechenizkiy},
  title = {PS3: Partition-based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data},
  booktitle = {Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Applied Data Science Track},
  year = {2020},
  file = {Fajri_ECMLPKDD2020rohtua.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Fajri_ECMLPKDD2020rohtua.pdf:PDF}
}
conference Liu, S., der Lee, T.V., Yaman, A., Atashgahi, Z., Ferraro, D., Sokar, G., Pechenizkiy, M. & Mocanu, D.C. (2020) Topological Insights into Sparse Neural Networks, In Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Research Track.

BibTeX: 
@inproceedings{Liu_ECMLPKDD2020raey,
  author = {Shiwei Liu and Tim Van der Lee and Anil Yaman and Zahra Atashgahi and Davide Ferraro and Ghada Sokar and Mykola Pechenizkiy and Decebal Constantin Mocanu},
  title = {Topological Insights into Sparse Neural Networks},
  booktitle = {Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Research Track},
  year = {2020},
  file = {Liu_ECMLPKDD2020.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Liu_ECMLPKDD2020.pdf:PDF}
}
conference Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B. & Burke, R. (2020) FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems, In Proceedings of 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2020).

BibTeX: 
@inproceedings{MansouryUmap2020.pdf,
  author = {Masoud Mansoury and Himan Abdollahpouri and Mykola Pechenizkiy and Bamshad Mobasher and Robin Burke},
  title = {FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems},
  booktitle = {In Proceedings of 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2020)},
  year = {2020},
  file = {MansouryUmap2020.pdf:https//arxiv.org/pdf/2005.01148.pdf:PDF}
}
workshop Mansoury, M., Abdollahpouri, H., Smith, J., Dehpanah, A., Pechenizkiy, M. & Mobasher, B. (2020) Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems, In Proceedings of the Thirty-Third International Florida Artificial Intelligence Research Society Conference, arXiv abs/2002.07786, AAAI Press, pp. 193-196.

BibTeX: 
@inproceedings{Mansoury2020.pdf,
  author = {Masoud Mansoury and Himan Abdollahpouri and Jessie Smith and Arman Dehpanah and Mykola Pechenizkiy and Bamshad Mobasher},
  title = {Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems},
  booktitle = {Proceedings of the Thirty-Third International Florida Artificial Intelligence Research Society Conference},
  publisher = {AAAI Press},
  year = {2020},
  volume = {arXiv abs/2002.07786},
  pages = {193--196},
  file = {Mansoury2020.pdf:https//arxiv.org/pdf/2002.07786.pdf:PDF}
}
preprint Atashgahi, Z., Sokar, G., van der Lee, T., Mocanu, E., Mocanu, D.C., Veldhuis, R.N.J. & Pechenizkiy, M. (2020) Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders, arXiv, abs/2012.00560.

BibTeX: 
@article{Atashgahi2020a,
  author = {Zahra Atashgahi and Ghada Sokar and Tim van der Lee and Elena Mocanu and Decebal Constantin Mocanu and Raymond N. J. Veldhuis and Mykola Pechenizkiy},
  title = {Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders},
  journal = {arXiv},
  year = {2020},
  volume = {abs/2012.00560},
  url = {https://arxiv.org/abs/2012.00560}
}
BibTeX: 
@article{Huang2020d,
  author = {Tianjin Huang and Vlado Menkovski and Yulong Pei and Mykola Pechenizkiy},
  title = {Bridging the Performance Gap between FGSM and PGD Adversarial Training},
  journal = {arXiv},
  year = {2020},
  volume = {abs/2011.05157},
  url = {https://arxiv.org/abs/2011.05157}
}
journal Liu, S., Mocanu, D., Ramapuram Matavalam, A.R., Pei, Y. & Pechenizkiy, M. (2020) Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware, accepted for publication in Neural Computing and Applications. Preprint published at arXiv:1901.09181.

Abstract: Microarray gene expression has widely attracted the eyes of the public as an efficient tool for cancer diagnosis and classification. However, the very-high dimensionality and the small number of samples make it difficult for traditional machine learning algorithms to address this problem due to the high amount of computations required and overfitting. So far, the existing approaches of processing microarray datasets are still far from satisfactory and they employ two phases, feature selection (or extraction) followed by a machine learning algorithm. In this paper, we show that MultiLayer Perceptrons (MLPs) with adaptive sparse connectivity can directly handle this problem without features selection. Tested on four datasets, our novel results demonstrate that deep learning methods can be applied directly also to high dimensional non-grid like data, while learning from a small amount of labeled examples with imbalanced classes and achieving better accuracy than the traditional two phases approach. Moreover, we have been able to create sparse MLP models with over one million neurons and to train them on a typical laptop without GPU. This is with two orders of magnitude more than the largest MLPs which can run currently on commodity hardware.
BibTeX: 
@article{Liu2019b,
  author = {Shiwei Liu and Decebal Mocanu and Ramapuram Matavalam, Amarsagar Reddy and Yulong Pei and Mykola Pechenizkiy},
  title = {Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware},
  booktitle = {accepted for publication in Neural Computing and Applications},
  year = {2020},
  number = {arXiv:1901.09181},
  file = {Liu2019b.pdf:https//arxiv.org/pdf/1901.09181.pdf:PDF}
}
workshop Huesca, J.M.G., van der Zon, S., van Ipenburg, W., Veldsink, J. & Pechenizkiy, M. (2020) Multi-View Risk Classification for Customer Due Diligence , In Proceedings of the AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services.

BibTeX: 
@inproceedings{Huesca2020,
  author = {Juan Manuel Gonzalez Huesca and Simon van der Zon and Werner van Ipenburg and Jan Veldsink and Mykola Pechenizkiy},
  title = {Multi-View Risk Classification for Customer Due Diligence },
  booktitle = {Proceedings of the AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services},
  year = {2020},
  file = {Huesca2020.pdf:https//aaai-kdf2020.github.io/assets/pdfs/kdf2020_paper_19.pdf:PDF}
}
journal Järvelä, S., Gašević, D., Seppänen, T., Pechenizkiy, M. & Kirschner, P.A. (2020) Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning, British Journal of Educational Technology, Wiley-Blackwell.

Abstract: Collaborative learning (CL) can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion and motivation) is key. Decades of research on self-regulated learning has advanced our understanding about the need for and complexity of those mediating processes in learning. Recent research has shown that it is not only the individual's but also the group's shared processes that matter and, thus, that regulation at the group level is critical for learning success. A problem here is that the “shared” processes in CL are invisible, which makes it almost impossible for researchers to study and understand them, for learners to recognize them and for teachers to support them. Traditionally, research has not been able to make these processes visible nor has it been able to collect data about them. With the aid of advanced technologies, signal processing and machine learning, we are on the verge of “seeing” these complex phenomena and understanding how they interact. We posit that technological solutions and digital tools available today and in the future will help advance the theory underlying the cognitive, metacognitive, emotional and social components of individual, peer and group learning when seen through a multidisciplinary lens. The aim of this paper is to discuss and demonstrate how multidisciplinary collaboration among the learning sciences, affective computing and machine learning is applied for understanding and facilitating CL.
BibTeX: 
@article{Jaervelae2020,
  author = {Sanna Järvelä and Dragan Gašević and Tapio Seppänen and Mykola Pechenizkiy and Kirschner, Paul A.},
  title = {Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning},
  journal = {British Journal of Educational Technology},
  publisher = {Wiley-Blackwell},
  year = {2020},
  doi = {http://doi.org/10.1111/bjet.12917}
}
preprint Huang, T., Menkovski, V., Pei, Y. & Pechenizkiy, M. (2020) Bridging the Performance Gap between FGSM and PGD Adversarial Training, arXiv, abs/2011.05157.

BibTeX: 
@article{Huang2020d,
  author = {Tianjin Huang and Vlado Menkovski and Yulong Pei and Mykola Pechenizkiy},
  title = {Bridging the Performance Gap between FGSM and PGD Adversarial Training},
  journal = {arXiv},
  year = {2020},
  volume = {abs/2011.05157},
  url = {https://arxiv.org/abs/2011.05157}
}
workshop Wang, Y., Menkovski, V., Wang, H., Du, X. & Pechenizkiy, M. (2020) Causal Discovery from Incomplete Data: A Deep Learning Approach, In Proceedings of Statistical Relational AI (StarAI) 2020 workshop @ AAAI 2020, abs/2001.05343.

BibTeX: 
@inproceedings{Wang2020,
  author = {Yuhao Wang and Vlado Menkovski and Hao Wang and Xin Du and Mykola Pechenizkiy},
  title = {Causal Discovery from Incomplete Data: A Deep Learning Approach},
  booktitle = {Proceedings of Statistical Relational AI (StarAI) 2020 workshop @ AAAI 2020},
  journal = {arXiv},
  year = {2020},
  volume = {abs/2001.05343},
  file = {Wang2020.pdf:https//arxiv.org/pdf/2001.05343.pdf:PDF}
}
journal Deng, Y., Bucchianico, A.D. & Pechenizkiy, M. (2020) Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model, Reliability Engineering and System Safety, 196, Elsevier.

Abstract: In modern industrial systems, sensor data reflecting the system health state are commonly used for the remaining useful lifetime (RUL) prediction, which are increasingly processed by modern deep learning based approaches recently. But these deep learning models do not automatically provide uncertainty information for the RUL prediction, hence this paper is motivated to introduce a novel approach that allows to control trade-off between prediction performance and knowledge about the uncertainty of the RUL prediction. The key aspect of our approach is to use a long short-term memory (LSTM) network as an expressive black-box predictor and the Wiener process as a surrogate to model the propagation of prediction uncertainty. The uncertainty propagation model is used to interactively train the RUL predictor. Our empirical results in a turbofan engine degradation simulation use case show that the surrogate Wiener propagation model can improve the near-failure prediction accuracy by sacrificing the far-to-failure prediction accuracy.
BibTeX: 
@article{Deng2020,
  author = {Yingjun Deng and Bucchianico, Alessandro Di and Mykola Pechenizkiy},
  title = {Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model},
  journal = {Reliability Engineering and System Safety},
  publisher = {Elsevier},
  year = {2020},
  volume = {196},
  doi = {http://doi.org/10.1016/j.ress.2019.106727},
  file = {Deng2020.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Deng2020.pdf:PDF}
}
journal Ahmadi, N., Pei, Y., Carrette, E., Aldenkamp, A.P. & Pechenizkiy, M. (2020) EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features, Brain Informatics, 7(1), pp. 1-22.

BibTeX: 
@article{AhmadiPCAP20,
  author = {Negar Ahmadi and Yulong Pei and Evelien Carrette and Albert P. Aldenkamp and Mykola Pechenizkiy},
  title = {EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features},
  journal = {Brain Informatics},
  year = {2020},
  volume = {7},
  number = {1},
  pages = {1-22},
  doi = {http://doi.org/10.1186/s40708-020-00107-z},
  file = {AhmadiPCAP2020.pdf:https//braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00107-z:PDF}
}
journal Mohammadpourfard, M., Weng, Y., Pechenizkiy, M., Tajdinian, M. & Mohammadi-Ivatloo, B. (2020) Ensuring cybersecurity of smart grid against data integrity attacks under concept drift, International Journal of Electrical Power and Energy Systems, 119, Elsevier.

Abstract: For achieving increasing artificial intelligence in future smart grids, a very precise state estimation (SE) is required as a prerequisite for many other key functionalities for successful monitoring and control. With increasing interconnection of utility network and internet, traditional state estimators are vulnerable to complex data integrity attacks, such as false data injection (FDI), bypassing existing bad data detection (BDD) schemes. While researchers propose detectors for FDI, such countermeasures neglect power state changes due to contingencies. As such an abrupt physical change negatively affects existing FDI detectors, they will provide incorrect classification of the new instances. To resolve the problem, we conducted analysis for a fundamental understanding of the differences between a physical grid change and data manipulation change. We use outage as an example and propose to analyze historical data followed by concept drift, focusing on distribution change. The key is to find critical lines to narrow down the scope. Techniques such as dimensionality reduction and statistical hypothesis testing are employed. The proposed approach is evaluated on the IEEE 14 bus system using load data from the New York independent system operator with two different attack scenarios: (1) attacks without concept drift, (2) attacks under concept drift. Numerical results show that the new method significantly increases the accuracy of the existing detection methods under concept drift.
BibTeX: 
@article{Mohammadpourfard2020,
  author = {Mostafa Mohammadpourfard and Yang Weng and Mykola Pechenizkiy and Mohsen Tajdinian and Behnam Mohammadi-Ivatloo},
  title = {Ensuring cybersecurity of smart grid against data integrity attacks under concept drift},
  journal = {International Journal of Electrical Power and Energy Systems},
  publisher = {Elsevier},
  year = {2020},
  volume = {119},
  doi = {http://doi.org/10.1016/j.ijepes.2020.105947},
  file = {Mohammadpourfard2020.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Mohammadpourfard2020.pdf:PDF}
}
conference Yaman, A., Iacca, G., Mocanu, D.C., Fletcher, G. & Pechenizkiy, M. (2020) Novelty Producing Synaptic Plasticity, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020).

BibTeX: 
@inproceedings{Yaman2020,
  author = {Anil Yaman and Giovanni Iacca and Decebal Constantin Mocanu and George Fletcher and Mykola Pechenizkiy},
  title = {Novelty Producing Synaptic Plasticity},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020)},
  year = {2020},
  file = {Yaman2020.pdf:https//arxiv.org/pdf/2002.03620.pdf:PDF}
}
workshop Oikarinen, E., Puolamäki, K., Khoshrou, S. & Pechenizkiy, M. (2020) Supervised human-guided data exploration, In Proceedings of International Workshops on Machine Learning and Knowledge Discovery in Databases @ ECML PKDD 2019, Communications in Computer and Information Science, Springer, pp. 85-101.

Abstract: An exploratory data analysis system should be aware of what a user already knows and what the user wants to know of the data. Otherwise it is impossible to provide the user with truly informative and useful views of the data. In our recently introduced framework for human-guided data exploration (Puolamäki et al. [20]), both the user’s knowledge and objectives are modelled as distributions over data, parametrised by tile constraints. This makes it possible to show the users the most informative views given their current knowledge and objectives. Often the data, however, comes with a class label and the user is interested only of the features informative related to the class. In non-interactive settings there exist dimensionality reduction methods, such as supervised PCA (Barshan et al. [1]), to make such visualisations, but no such method takes the user’s knowledge or objectives into account. Here, we formulate an information criterion for supervised human-guided data exploration to find the most informative views about the class structure of the data by taking both the user’s current knowledge and objectives into account. We study experimentally the scalability of our method for interactive use, and stability with respect to the size of the class of interest. We show that our method gives understandable and useful results when analysing real-world datasets, and a comparison to SPCA demonstrates the effect of the user’s background knowledge. The implementation will be released as an open source software library.
BibTeX: 
@inproceedings{Oikarinen2020,
  author = {Emilia Oikarinen and Kai Puolamäki and Samaneh Khoshrou and Mykola Pechenizkiy},
  title = {Supervised human-guided data exploration},
  booktitle = {Proceedings of International Workshops on Machine Learning and Knowledge Discovery in Databases @ ECML PKDD 2019},
  editor = {Peggy Cellier and Kurt Driessens},
  publisher = {Springer},
  year = {2020},
  pages = {85--101},
  doi = {http://doi.org/10.1007/978-3-030-43823-4_8},
  file = {Oikarinen2020.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Oikarinen2020.pdf:PDF}
}
workshop Weerts, H.J.P., van Ipenburg, W. & Pechenizkiy, M. (2019) A Human-Grounded Evaluation of SHAP for Alert Processing, In Explainable AI @ KDD 2019, abs/1907.03324.

BibTeX: 
@inproceedings{Hilde2019,
  author = {Hilde J. P. Weerts and Werner van Ipenburg and Mykola Pechenizkiy},
  title = {A Human-Grounded Evaluation of SHAP for Alert Processing},
  booktitle = {Explainable AI @ KDD 2019},
  year = {2019},
  volume = {abs/1907.03324},
  file = {Hilde2019.pdf:https//arxiv.org/pdf/1907.03324.pdf:PDF}
}
conference Khoshrou, S. & Pechenizkiy, M. (2019) Adaptive Long-Term Ensemble Learning from Multiple High-Dimensional Time-Series, In Proceedings of 22nd International Conference Discovery Science, DS 2019, Lecture Notes in Computer Science, Springer, pp. 511-521.

Abstract: Learning from multiple time-series over an unbounded time-frame has received less attention despite the key applications (such as video analysis, home-assisted) generating this data. Inspired by never-ending approaches, this paper presents an algorithm to continuously learn from multiple high-dimensional un-regulated time-series, in a framework based on ensembles which with respect to drift level develops over time in order to reflect the latest concepts. Here, we explicitly look into video surveillance problem as one of the main sources of high-dimensional data in daily life and extensive experiments are conducted on multiple datasets, that demonstrate the advantages of the proposed framework in terms of accuracy and complexity over several baseline approaches.
BibTeX: 
@inproceedings{Khoshrou2019,
  author = {Samaneh Khoshrou and Mykola Pechenizkiy},
  title = {Adaptive Long-Term Ensemble Learning from Multiple High-Dimensional Time-Series},
  booktitle = {Proceedings of 22nd International Conference Discovery Science, DS 2019},
  editor = {Kralj Novak, Petra and Sašo Džeroski and Tomislav Šmuc},
  publisher = {Springer},
  year = {2019},
  pages = {511--521},
  doi = {http://doi.org/10.1007/978-3-030-33778-0_38},
  file = {Khoshrou2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Khoshrou2019.pdf:PDF}
}
technical report Du, X., Sun, L., Duivesteijn, W., Nikolaev, A. & Pechenizkiy, M. (2019) Adversarial balancing-based representation learning for causal effect inference with observational data, arXiv:1904.13335.

Abstract: Learning causal effects from observational data greatly benefits a variety of domains such as healthcare, education and sociology. For instance, one could estimate the impact of a policy to decrease unemployment rate. The central problem for causal effect inference is dealing with the unobserved counterfactuals and treatment selection bias. The state-of-the-art approaches focus on solving these problems by balancing the treatment and control groups. However, during the learning and balancing process, highly predictive information from the original covariate space might be lost. In order to build more robust estimators, we tackle this information loss problem by presenting a method called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on the recent advances in deep learning. ABCEI uses adversarial learning to balance the distributions of treatment and control group in the latent representation space, without any assumption on the form of the treatment selection/assignment function. ABCEI preserves useful information for predicting causal effects under the regularization of a mutual information estimator. We conduct various experiments on several synthetic and real-world datasets. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches.
BibTeX: 
@techreport{Du2019,
  author = {Xin Du and Lei Sun and Wouter Duivesteijn and Alexander Nikolaev and Mykola Pechenizkiy},
  title = {Adversarial balancing-based representation learning for causal effect inference with observational data},
  year = {2019},
  number = {arXiv:1904.13335},
  file = {Du2019.pdf:https//arxiv.org/pdf/1904.13335.pdf:PDF}
}
workshop Mansoury, M., Mobasher, B., Burke, R. & Pechenizkiy, M. (2019) Bias disparity in collaborative recommendation: algorithmic evaluation and comparison, In Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments @ RecSys 2019, CEUR Workshop Proceedings, CEUR-WS.org.

Abstract: Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group’s preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.
BibTeX: 
@inproceedings{Mansoury2019,
  author = {Masoud Mansoury and Bamshad Mobasher and Robin Burke and Mykola Pechenizkiy},
  title = {Bias disparity in collaborative recommendation: algorithmic evaluation and comparison},
  booktitle = {Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments @ RecSys 2019},
  editor = {Robin Burke and Himan Abdollahpouri and Edward Malthouse},
  publisher = {CEUR-WS.org},
  year = {2019},
  file = {Mansoury2019.pdf:https//arxiv.org/pdf/1908.00831.pdf:PDF}
}
technical report Ni'mah, I., Menkovski, V. & Pechenizkiy, M. (2019) BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation, arXiv 1909.09485.

BibTeX: 
@techreport{Nimah2019,
  author = {Iftitahu Ni'mah and Vlado Menkovski and Mykola Pechenizkiy},
  title = {BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation},
  year = {2019},
  number = {arXiv 1909.09485},
  file = {Nimah2019.pdf:https//arxiv.org/pdf/1909.09485.pdf:PDF}
}
conference Weerts, H.J.P., van Ipenburg, W. & Pechenizkiy, M. (2019) Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models, abs/1907.03334.

BibTeX: 
@inproceedings{Weerts2019,
  author = {Hilde J. P. Weerts and Werner van Ipenburg and Mykola Pechenizkiy},
  title = {Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models},
  journal = {arXiv},
  year = {2019},
  volume = {abs/1907.03334},
  file = {Weerts2019.pdf:https//arxiv.org/pdf/1907.03334.pdf:PDF}
}
journal Zhang, J., Zhu, K., Pei, Y., Fletcher, G. & Pechenizkiy, M. (2019) Cluster-preserving sampling from fully-dynamic streaming graphs, Information Sciences, 482, Elsevier, pp. 279-300.

Abstract: Current sampling techniques on graphs (i.e., network-structured data) mainly study static graphs and suppose that the whole graph is available at all times. However, a surge of graphs are becoming too large-scale and/or fully-dynamic (i.e., nodes and edges are added or deleted arbitrarily) to be handled with small memory footprint. Moreover, the intrinsic property (i.e., clustering structure) has been ignored and is not preserved well when the sampling performs. To solve these issues, we propose a Cluster-preserving Partially Induced Edge Sampling (CPIES) algorithm that dynamically keep up-to-date samples in an online fashion and preserve the inherent clustering structure in streaming graphs. The experiments on various synthetic and real-world graphs demonstrated that the proposed CPIES algorithm is capable of preserving the inherent clustering structure while sampling from the fully-dynamic streaming graphs, and performs better than other competing algorithms in cluster-related properties.
BibTeX: 
@article{Zhang2019,
  author = {Jianpeng Zhang and Kaijie Zhu and Yulong Pei and George Fletcher and Mykola Pechenizkiy},
  title = {Cluster-preserving sampling from fully-dynamic streaming graphs},
  journal = {Information Sciences},
  publisher = {Elsevier},
  year = {2019},
  volume = {482},
  pages = {279--300},
  doi = {http://doi.org/10.1016/j.ins.2019.01.011},
  file = {Zhang2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Zhang2019.pdf:PDF}
}
journal Ahmadi, N., Pei, Y. & Pechenizkiy, M. (2019) Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model, Physica A: Statistical and Theoretical Physics, 520, Elsevier, pp. 289-308.

Abstract: Volume conduction in the brain may influence the synchronization between EEG signals considerably, as it may lead to detection of spurious functional couplings among the recording channels. It has been shown that the volume conduction effect can be approximated as a linear mixing of the electrical fields of the brain regions. In this paper, we investigate the reliability of various synchronization measures in the presence of the linear superposition in EEG time series. For this purpose, we applied linear mixing to artificially generated EEG times series using the Kuramoto model of coupled phase oscillators, which represents the behavior of coupled systems with local interactions at the fundamental level. Our simulation results showed that the phase-lag index and the synchronization measures based on the visibility graph algorithms were less sensitive to the linear mixing effect and could predict the coupling degree correctly even with strongly overlapping signals. The results of our further data analyses demonstrated the effect of linear superposition in time series on the behavior of various complex network measures. For each case, we provide recommendations for proper choices of synchronization measures to obtain complex network characteristics that are minimally sensitive to linear mixing.
BibTeX: 
@article{Ahmadi2019,
  author = {Negar Ahmadi and Yulong Pei and Mykola Pechenizkiy},
  title = {Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model},
  journal = {Physica A: Statistical and Theoretical Physics},
  publisher = {Elsevier},
  year = {2019},
  volume = {520},
  pages = {289--308},
  doi = {http://doi.org/10.1016/j.physa.2019.01.003},
  file = {Ahmadi2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Ahmadi2019.pdf:PDF}
}
technical report Yaman, A., Mocanu, D., Iacca, G., Coler, M., Fletcher, G. & Pechenizkiy, M. (2019) Evolving plasticity for autonomous learning under changing environmental conditions, arXiv:1904.01709.

Abstract: A fundamental aspect of learning in biological neural networks (BNNs) is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property based on the local activation of neurons. In this work, we employ genetic algorithms to evolve local learning rules, from Hebbian perspective, to produce autonomous learning under changing environmental conditions. Our evolved synaptic plasticity rules are capable of performing synaptic updates in distributed and self-organized fashion, based only on the binary activation states of neurons, and a reinforcement signal received from the environment. We demonstrate the learning and adaptation capabilities of the ANNs modified by the evolved plasticity rules on a foraging task in a continuous learning settings. Our results show that evolved plasticity rules are highly efficient at adapting the ANNs to task under changing environmental conditions.
BibTeX: 
@techreport{Yaman2019a,
  author = {Anil Yaman and Decebal Mocanu and Giovanni Iacca and Matt Coler and George Fletcher and Mykola Pechenizkiy},
  title = {Evolving plasticity for autonomous learning under changing environmental conditions},
  year = {2019},
  number = {arXiv:1904.01709},
  file = {Yaman2019a.pdf:https//arxiv.org/pdf/1904.01709.pdf:PDF}
}
workshop van der Zon, S.B., Duivesteijn, W., van Ipenburg, W., Veldsink, J. & Pechenizkiy, M. (2019) ICIE 1.0: a novel tool for interactive contextual interaction explanations, In Proceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018, Lecture Notes in Computer Science, Springer, pp. 81-94.

Abstract: With the rise of new laws around privacy and awareness, explanation of automated decision making becomes increasingly important. Nowadays, machine learning models are used to aid experts in domains such as banking and insurance to find suspicious transactions, approve loans and credit card applications. Companies using such systems have to be able to provide the rationale behind their decisions; blindly relying on the trained model is not sufficient. There are currently a number of methods that provide insights in models and their decisions, but often they are either good at showing global or local behavior. Global behavior is often too complex to visualize or comprehend, so approximations are shown, and visualizing local behavior is often misleading as it is difficult to define what local exactly means (i.e. our methods don’t “know” how easily a feature-value can be changed; which ones are flexible, and which ones are static). We introduce the ICIE framework (Interactive Contextual Interaction Explanations) which enables users to view explanations of individual instances under different contexts. We will see that various contexts for the same case lead to different explanations, revealing different feature interactions.
BibTeX: 
@inproceedings{vanderZon2019,
  author = {van der Zon, Simon B. and Wouter Duivesteijn and van Ipenburg, Werner and Jan Veldsink and Mykola Pechenizkiy},
  title = {ICIE 1.0: a novel tool for interactive contextual interaction explanations},
  booktitle = {Proceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018},
  editor = {Anna Monreale and Carlos Alzate},
  publisher = {Springer},
  year = {2019},
  pages = {81--94},
  doi = {http://doi.org/10.1007/978-3-030-13463-1_6},
  file = {vanderZon2019.pdf:http//wwwis.win.tue.nl/ wouter/Publ/W6-ICIE.pdf:PDF}
}
conference Pei, Y., Zhang, J., Fletcher, G. & Pechenizkiyc, M. (2019) Infinite motif stochastic blockmodel for role discovery in networks, In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, ACM, pp. 456-459.

Abstract: Role/block discovery is an essential task in network analytics so it has attracted significant attention recently. Previous studies on role discovery either relied on first or second-order structural information to group nodes but neglected the higher-order information or required the number of roles/blocks as the input which may be unknown in practice. To overcome these limitations, in this paper we propose a novel generative model, infinite motif stochastic blockmodel (IMM), for role discovery in networks. IMM takes advantage of high-order motifs in the generative process and it is a nonparametric Bayesian model which can automatically infer the number of roles. To validate the effectiveness of IMM, we conduct experiments on synthetic and real-world networks. The obtained results demonstrate IMM outperforms other blockmodels in role discovery task.
BibTeX: 
@inproceedings{PeiZhangetal2019,
  author = {Yulong Pei and Jianpeng Zhang and George Fletcher and Mykola Pechenizkiyc},
  title = {Infinite motif stochastic blockmodel for role discovery in networks},
  booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019},
  editor = {Francesca Spezzano and Wei Chen and Xiaokui Xiao},
  publisher = {ACM},
  year = {2019},
  pages = {456--459},
  doi = {http://doi.org/10.1145/3341161.3342921},
  file = {PeiZhangetal2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PeiZhangetal2019.pdf:PDF}
}
technical report Liu, S., Mocanu, D. & Pechenizkiy, M. (2019) Intrinsically sparse long short-term memory networks, arXiv:1901.09208.

Abstract: Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a wide range of tasks. Its outstanding performance is guaranteed by the long-term memory ability which matches the sequential data perfectly and the gating structure controlling the information flow. However, LSTMs are prone to be memory-bandwidth limited in realistic applications and need an unbearable period of training and inference time as the model size is ever-increasing. To tackle this problem, various efficient model compression methods have been proposed. Most of them need a big and expensive pre-trained model which is a nightmare for resource-limited devices where the memory budget is strictly limited. To remedy this situation, in this paper, we incorporate the Sparse Evolutionary Training (SET) procedure into LSTM, proposing a novel model dubbed SET-LSTM. Rather than starting with a fully-connected architecture, SET-LSTM has a sparse topology and dramatically fewer parameters in both phases, training and inference. Considering the specific architecture of LSTMs, we replace the LSTM cells and embedding layers with sparse structures and further on, use an evolutionary strategy to adapt the sparse connectivity to the data. Additionally, we find that SET-LSTM can provide many different good combinations of sparse connectivity to substitute the overparameterized optimization problem of dense neural networks. Evaluated on four sentiment analysis classification datasets, the results demonstrate that our proposed model is able to achieve usually better performance than its fully connected counterpart while having less than 4% of its parameters.
BibTeX: 
@techreport{Liu2019a,
  author = {Shiwei Liu and Decebal Mocanu and Mykola Pechenizkiy},
  title = {Intrinsically sparse long short-term memory networks},
  year = {2019},
  number = {arXiv:1901.09208},
  file = {Liu2019a.pdf:https//arxiv.org/pdf/1901.09208.pdf:PDF}
}
conference Pei, Y., Fletcher, G. & Pechenizkiy, M. (2019) Joint role and community detection in networks via L2,1 norm regularized nonnegative matrix tri-factorization, In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, ACM, pp. 168-175.

Abstract: Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.
BibTeX: 
@inproceedings{Pei2019a,
  author = {Yulong Pei and George Fletcher and Mykola Pechenizkiy},
  title = {Joint role and community detection in networks via L2,1 norm regularized nonnegative matrix tri-factorization},
  booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019},
  editor = {Francesca Spezzano and Wei Chen and Xiaokui Xiao},
  publisher = {ACM},
  year = {2019},
  pages = {168--175},
  doi = {http://doi.org/10.1145/3341161.3342886},
  file = {PeiAsonam2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PeiAsonam2019.pdf:PDF}
}
workshop Zheng, S., van der Zon, S.P., Pechenizkiy, M., de Campos, C.P., van Ipenburg, W. & de Harder, H. (2019) Labelless Concept Drift Detection and Explanation, In Proceedings of NeurIPS 2019 Workshop on Robust AI in Financial Service.

BibTeX: 
@inproceedings{Zheng2019,
  author = {Shihao Zheng and Simon P. van der Zon and Mykola Pechenizkiy and Cassio P. de Campos and Werner van Ipenburg and Hennie de Harder},
  title = {Labelless Concept Drift Detection and Explanation},
  booktitle = {Proceedings of NeurIPS 2019 Workshop on Robust AI in Financial Service},
  year = {2019},
  file = {Zheng2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Zheng2019.pdf:PDF}
}
conference Yaman, A., Iacca, G., Mocanu, D.C., Fletcher, G.H.L. & Pechenizkiy, M. (2019) Learning with delayed synaptic plasticity, In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO 2019), ACM, pp. 152-160.

Abstract: The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses based on the neuron activations and reinforcement signals. However, the distal reward problem arises when the reinforcement signals are not available immediately after each network output to associate the neuron activations that contributed to receiving the reinforcement signal. In this work, we extend Hebbian plasticity rules to allow learning in distal reward cases. We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse to keep track of the activation of the neurons. Delayed reinforcement signals are provided after each episode relative to the networks' performance during the previous episode. We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed reinforcement signals. We compare DSP with an analogous hill climbing algorithm that does not incorporate domain knowledge introduced with the NATs, and show that the synaptic updates performed by the DSP rules demonstrate more effective training performance relative to the HC algorithm.
BibTeX: 
@inproceedings{Yaman2019,
  author = {Anil Yaman and Giovanni Iacca and Mocanu, Decebal Constantin and Fletcher, George H.L. and Mykola Pechenizkiy},
  title = {Learning with delayed synaptic plasticity},
  booktitle = {Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO 2019)},
  publisher = {ACM},
  year = {2019},
  pages = {152--160},
  doi = {http://doi.org/10.1145/3321707.3321723},
  file = {Yaman2019.pdf:https//arxiv.org/pdf/1903.09393.pdf:PDF}
}
workshop Liu, S., Mocanu, D. & Pechenizkiy, M. (2019) On improving deep learning generalization with adaptive sparse connectivity, In Proceedings of ICML 2019 Workshop on Understanding and Improving Generalization in Deep Learning.

Abstract: Large neural networks are very successful in various tasks. However, with limited data, the generalization capabilities of deep neural networks are also very limited. In this paper, we empirically start showing that intrinsically sparse neural networks with adaptive sparse connectivity, which by design have a strict parameter budget during the training phase, have better generalization capabilities than their fully-connected counterparts. Besides this, we propose a new technique to train these sparse models by combining the Sparse Evolutionary Training (SET) procedure with neurons pruning. Operated on MultiLayer Perceptron (MLP) and tested on 15 datasets, our proposed technique zeros out around 50% of the hidden neurons during training, while having a linear number of parameters to optimize with respect to the number of neurons. The results show a competitive classification and generalization performance.
BibTeX: 
@inproceedings{Liu2019,
  author = {Shiwei Liu and Decebal Mocanu and Mykola Pechenizkiy},
  title = {On improving deep learning generalization with adaptive sparse connectivity},
  booktitle = {Proceedings of ICML 2019 Workshop on Understanding and Improving Generalization in Deep Learning},
  year = {2019},
  file = {Liu2019.pdf:https//arxiv.org/pdf/1906.11626.pdf:PDF}
}
technical report Mansoury, M., Abdollahpouri, H., Rombouts, J. & Pechenizkiy, M. (2019) The Relationship between the Consistency of Users' Ratings and Recommendation Calibration, arXiv 1911.00852.

BibTeX: 
@techreport{Mansoury2019,
  author = {Masoud Mansoury and Himan Abdollahpouri and Joris Rombouts and Mykola Pechenizkiy},
  title = {The Relationship between the Consistency of Users' Ratings and Recommendation Calibration},
  year = {2019},
  number = {arXiv 1911.00852},
  file = {Mansoury2019.pdf:https//arxiv.org/pdf/1911.00852.pdf:PDF}
}
conference Wang, Y., Menkovski, V., Ho, I.W.H. & Pechenizkiy, M. (2019) VANET meets deep learning: the effect of packet loss on the object detection performance, In Proceedings of IEEE 89th Vehicular Technology Conference, VTC Spring 2019, pp. 1-5.

Abstract: The integration of machine learning and inter- vehicle communications enables various active safety measures in internet-of-vehicles. Specifically, the environmental perception is processed by the deep learning module from vehicular sensor data, and the extended perception range is achieved by exchanging traffic-related information through inter-vehicle communications. Under such condition, the intelligent vehicles can not only percept the surrounding environment from self-collected sensor data, but also expand their perception range through the information sharing mechanism of Vehicular Ad-hoc Network (VANET). However, the dynamic urban environment in VANET leads to a number of issues, such as the effect of packet loss on the real-time perception accuracy of the received sensor data. In this work, we propose a point cloud object detection module via an end-to-end deep learning system and enable wireless communications between vehicles to enhance driving safety and facilitate real-time 3D mapping construction. Besides, we build a semi- realistic traffic scenario based on the Mong Kok district in Hong Kong to analyze the network performance of data dissemination under the dynamic environment. Finally, we evaluate the impact of data loss on the deep-learning-based object detection performance. Our results indicate that data loss beyond 50% (which is a common scene based on our simulation) can lead to a rapid decline of the object detection accuracy.
BibTeX: 
@inproceedings{Wang2019,
  author = {Yuhao Wang and Vlado Menkovski and Ho, Ivan Wang Hei and Mykola Pechenizkiy},
  title = {VANET meets deep learning: the effect of packet loss on the object detection performance},
  booktitle = {Proceedings of IEEE 89th Vehicular Technology Conference, VTC Spring 2019},
  year = {2019},
  pages = {1--5},
  doi = {http://doi.org/10.1109/VTCSpring.2019.8746657},
  file = {Wang2019.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Wang2019.pdf:PDF}
}
journal Zhang, J., Pei, Y., Fletcher, G. & Pechenizkiy, M. (2018) A bounded-size clustering algorithm on fully-dynamic streaming graphs, Intelligent Data Analysis, 22(5), IOS Press, pp. 1039-1058.

Abstract: Many contemporary data sources in a variety of domains can naturally be represented as fully-dynamic streaming graphs. How to design an efficient online streaming clustering algorithm on such graphs is of great concern. However, existing clustering approaches are inappropriate for this specific task because: (1) static clustering approaches require expensive computational cost to cluster the graph for each update and (2) the existing streaming clustering neither could fully support insertion/deletion of edges nor take temporal information into account. To tackle these issues, in this work, firstly we propose an appropriate streaming clustering model and design two new core components: streaming reservoir and cluster manager. Then we present an evolution-aware bounded-size clustering algorithm to handle the edge additions/deletions. It requires the clusters to satisfy the maximum cluster-size constraint, and maintains the recency of edges in the temporal sequence and gives high priority to the recent edges in each cluster. The experimental results show that the proposed BSC algorithm outperforms current online algorithms and is capable to keep track of the evolution of graphs. Furthermore, it obtains almost one order of magnitude higher throughput than the state-of-the-art algorithms.
BibTeX: 
@article{Zhang2018,
  author = {Jianpeng Zhang and Yulong Pei and George Fletcher and Mykola Pechenizkiy},
  title = {A bounded-size clustering algorithm on fully-dynamic streaming graphs},
  journal = {Intelligent Data Analysis},
  publisher = {IOS Press},
  year = {2018},
  volume = {22},
  number = {5},
  pages = {1039--1058},
  doi = {http://doi.org/10.3233/IDA-173573},
  file = {Zhang2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Zhang2018.pdf:PDF}
}
journal Luna, J.M., Padillo, F., Pechenizkiy, M. & Ventura, S. (2018) Apriori versions based on MapReduce for mining frequent patterns on big data, IEEE Transactions on Cybernetics, 48(10), Institute of Electrical and Electronics Engineers, pp. 2851-2865.

Abstract: Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing item-set in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed. Finally, a last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the performance of the proposed algorithms, a varied collection of big data datasets have been considered, comprising up to 3.10 18 transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.
BibTeX: 
@article{Luna2018a,
  author = {J.M. Luna and F. Padillo and M. Pechenizkiy and S. Ventura},
  title = {Apriori versions based on MapReduce for mining frequent patterns on big data},
  journal = {IEEE Transactions on Cybernetics},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2018},
  volume = {48},
  number = {10},
  pages = {2851--2865},
  doi = {http://doi.org/10.1109/TCYB.2017.2751081},
  file = {Luna2018a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Luna2018a.pdf:PDF}
}
journal Ahmadi, N., Besseling, R.M.H. & Pechenizkiy, M. (2018) Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series, Social Network Analysis and Mining, 8(1), Springer.

Abstract: Finding synchronization between the outputs of a dynamic system, which are represented mostly as time series, helps to characterize the system activities during an occurrence. An important issue in analyzing time series is that they may behave chaotically or stochastically. Therefore, applying a reliable synchronization measure which can capture the dynamic features of the system helps to quantify the interdependencies between time series, correctly. In this paper, we employ similarity measures based on visibility graph (VG) algorithms as an alternative and radically different way to measure the synchronization between time series. We assess the performance of VG-based similarity measures on chaotic, noisy and stochastic time series. In our experiments, we use the Rössler system and the noisy Hénon map as representative instances of chaotic systems, and the Kuramoto model for studying detection of synchronization between stochastic time series. Our study suggests that the similarity measure based on the horizontal VG algorithm should be favored to other measures for detecting synchronization between chaotic and stochastic time series.
BibTeX: 
@article{Ahmadi2018,
  author = {Negar Ahmadi and Besseling, Rene M.H. and Mykola Pechenizkiy},
  title = {Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series},
  journal = {Social Network Analysis and Mining},
  publisher = {Springer},
  year = {2018},
  volume = {8},
  number = {1},
  doi = {http://doi.org/10.1007/s13278-018-0526-x},
  file = {Ahmadi2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Ahmadi2018.pdf:PDF}
}
special issue Zhang, J., Zhu, K., Pei, Y., Fletcher, G. & Pechenizkiy, M. (2018) Clustering-structure representative sampling from graph streams, In Complex Networks and Their Applications VI, Studies in Computational Intelligence, Springer, pp. 265-277.

Abstract: Most existing sampling algorithms on graphs (i.e., network-structured data) focus on sampling from memory-resident static graphs and assume the entire graphs are always available. However, the graphs encountered in modern applications are often too large and/or too dynamic to be processed with limited memory. Furthermore, existing sampling techniques are inadequate for preserving the inherent clustering structure, which is an essential property of complex networks. To tackle these problems, we propose a new sampling algorithm that dynamically maintains a representative sample and is capable of retaining clustering structure in graph streams at any time. Performance of the proposed algorithm is evaluated through empirical experiments using real-world networks. The experimental results have shown that our proposed CPIES algorithm can produce clustering-structure representative samples and outperforms current online sampling algorithms.
BibTeX: 
@inproceedings{Zhang2018a,
  author = {Jianpeng Zhang and Kaijie Zhu and Yulong Pei and George Fletcher and Mykola Pechenizkiy},
  title = {Clustering-structure representative sampling from graph streams},
  booktitle = {Complex Networks and Their Applications VI},
  editor = {C. Cherifi and H. Cherifi and M. Karsai and M. Musulesi},
  publisher = {Springer},
  year = {2018},
  pages = {265--277},
  doi = {http://doi.org/10.1007/978-3-319-72150-7_22},
  file = {Zhang2018a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Zhang2018a.pdf:PDF}
}
technical report Zeev Ben Mordehay, O., Duivesteijn, W. & Pechenizkiy, M. (2018) Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally, arXiv:1808.07243.

BibTeX: 
@inproceedings{Zeev2018,
  author = {Oren Zeev Ben Mordehay and Wouter Duivesteijn and Mykola Pechenizkiy},
  title = {Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally},
  year = {2018},
  volume = {arXiv:1808.07243},
  file = {Zeev2018.pdf:https//arxiv.org/pdf/1808.07243:PDF}
}
conference Pei, Y., Zhang, J., Fletcher, G. & Pechenizkiy, M. (2018) DyNMF: Role analytics in dynamic social networks, In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, International Joint Conferences on Artificial Intelligence, pp. 3818-3824.

Abstract: Roles of nodes in a social network (SN) represent their functions, responsibilities or behaviors within the SN. Roles typically evolve over time, making role analytics a challenging problem. Previous studies either neglect role transition analysis or perform role discovery and role transition learning separately, leading to inefficiencies and limited transition analysis. We propose a novel dynamic non-negative matrix factorization (DyNMF) approach to simultaneously discover roles and learn role transitions. DyNMF explicitly models temporal information by introducing a role transition matrix and clusters nodes in SNs from two views: the current view and the historical view. The current view captures structural information from the current SN snapshot and the historical view captures role transitions by looking at roles in past SN snapshots. DyNMF efficiently provides more effective analytics capabilities, regularizing roles by temporal smoothness of role transitions and reducing uncertainties and inconsistencies between snapshots. Experiments on both synthetic and real-world SNs demonstrate the advantages of DyNMF in discovering and predicting roles and role transitions.
BibTeX: 
@inproceedings{Pei2018,
  author = {Yulong Pei and Jianpeng Zhang and George Fletcher and Mykola Pechenizkiy},
  title = {DyNMF: Role analytics in dynamic social networks},
  booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018},
  editor = {Jerome Lang},
  publisher = {International Joint Conferences on Artificial Intelligence},
  year = {2018},
  pages = {3818--3824},
  doi = {http://doi.org/10.24963/ijcai.2018/531},
  file = {Pei2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Pei2018.pdf:PDF}
}
conference Du, X., Duivesteijn, W., Klabbers, M.D. & Pechenizkiy, M. (2018) ELBA: Exceptional Learning Behavior Analysis, In Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018, pp. 312-318.

Abstract: Behavioral records collected through course assessments, peer assignments, and programming assignments in Massive Open Online Courses (MOOCs) provide multiple views about a student’s study style. Study behavior is correlated with whether or not the student can get a certificate or drop out from a course. It is of predominant importance to identify the particular behavioral patterns and establish an accurate predictive model for the learning results, so that tutors can give well-focused assistance and guidance on specific students. However, the behavioral records of individuals are usually very sparse; behavioral records between individuals are inconsistent in time and skewed in contents. These remain big challenges for the state-of-the-art methods. In this paper, we engage the concept of subgroup as a trade-off to overcome the sparsity of individual behavioral records and inconsistency between individuals. We employ the framework of Exceptional Model Mining (EMM) to discover exceptional student behavior. Various model classes of EMM are applied on dropout rate analysis, correlation analysis between length of learning behavior sequence and course grades, and passing state prediction analysis. Qualitative and quantitative experimental results on real MOOCs datasets show that our method can discover significantly interesting learning behavioral patterns of students.
BibTeX: 
@inproceedings{Du2018,
  author = {X. Du and W. Duivesteijn and M.D. Klabbers and M. Pechenizkiy},
  title = {ELBA: Exceptional Learning Behavior Analysis},
  booktitle = {Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018},
  year = {2018},
  pages = {312--318},
  file = {Du2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Du2018.pdf:PDF}
}
conference Ahmadi, N., Carrette, E., Aldenkamp, A.P. & Pechenizkiy, M. (2018) Finding predictive EEG complexity features for classification of epileptic and psychogenic nonepileptic seizures using imperialist competitive algorithm, In Proceedings of 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Institute of Electrical and Electronics Engineers, pp. 164-169.

Abstract: In this study, the imperialist competitive algorithm (ICA) is applied for classification of epileptic seizure and psychogenic nonepileptic seizure (PNES). For this purpose, after decomposing the EEG signal into five sub-bands and extracting some complexity features of EEG, the ICA is applied to find the predictive feature subset that maximizes the classification performance in the frequency spectrum. Results show that the spectral entropy and Renyi entropy are the most important EEG features as they are always appeared in the best feature subsets when applying different classifiers. Also, it is observed that the SVM-RBF and SVM-linear models are the best classifiers resulting in highest performance metrics compared to other classifiers. Our study shows that the reported algorithm is able to classify the epileptic seizure and PNES with a very high classification metrics.
BibTeX: 
@inproceedings{Ahmadi2018a,
  author = {N. Ahmadi and Evelien Carrette and A.P. Aldenkamp and M. Pechenizkiy},
  title = {Finding predictive EEG complexity features for classification of epileptic and psychogenic nonepileptic seizures using imperialist competitive algorithm},
  booktitle = {Proceedings of 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2018},
  pages = {164--169},
  doi = {http://doi.org/10.1109/CBMS.2018.00036},
  file = {Ahmadi2018a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Ahmadi2018a.pdf:PDF}
}
conference Yaman, A., Mocanu, D.C., Iacca, G., Fletcher, G. & Pechenizkiy, M. (2018) Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution, In Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), ACM, pp. 569-576.

Abstract: Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architectures tend to be large. Neuroevolution is an effective approach for optimizing ANNs; however, there are two bottlenecks that make their application challenging in case of high-dimensional networks using direct encoding. First, classic evolutionary algorithms tend not to scale well for searching large parameter spaces; second, the network evaluation over a large number of training instances is in general time-consuming. In this work, we propose an approach called the Limited Evaluation Cooperative Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize high-dimensional ANNs. The proposed method aims to optimize the pre-synaptic weights of each post-synaptic neuron in different subpopulations using a Cooperative Co-evolutionary Differential Evolution algorithm, and employs a limited evaluation scheme where fitness evaluation is performed on a relatively small number of training instances based on fitness inheritance. We test LECCDE on three datasets with various sizes, and our results show that cooperative co-evolution significantly improves the test error comparing to standard Differential Evolution, while the limited evaluation scheme facilitates a significant reduction in computing time.
BibTeX: 
@inproceedings{Yaman2018,
  author = {Anil Yaman and Mocanu, Decebal Constantin and Giovanni Iacca and George Fletcher and Mykola Pechenizkiy},
  title = {Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution},
  booktitle = {Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO 2018)},
  publisher = {ACM},
  year = {2018},
  pages = {569--576},
  doi = {http://doi.org/10.1145/3205455.3205555},
  file = {Yaman2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Yaman2018.pdf:PDF}
}
workshop Xiong, W., Ni'mah, I., Huesca, J.M.G., van Ipenburg, W., Veldsink, J. & Pechenizkiy, M. (2018) "Looking deeper into deep learning model: attribution-based explanations of TextCNN", In Proceedings of NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services : the Impact of Fairness, Explainability, Accuracy, and Privacy.

Abstract: Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method. This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction. In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set). Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks. Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations. Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model's final prediction.
BibTeX: 
@conference{Xiong2018,
  author = {Wenting Xiong and Iftitahu Ni'mah and Huesca, Juan M. G. and van Ipenburg, Werner and Jan Veldsink and Mykola Pechenizkiy},
  title = {Looking deeper into deep learning model: attribution-based explanations of TextCNN},
  booktitle = {Proceedings of NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services : the Impact of Fairness, Explainability, Accuracy, and Privacy},
  year = {2018},
  note = {NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services : the Impact of Fairness, Explainability, Accuracy, and Privacy ; Conference date: 07-12-2018 Through 07-12-2018},
  pdf = {Xiong2018.pdf::PDF}  
}
workshop Khoshrou, S. & Pechenizkiy, M. (2018) MDL-based Development of Ensembles with Active Learning over Evolving Data Streams, In Proceedings of 4th Workshop on Mining and Learning from Time Series (Milet's) @ KDD 2018.

BibTeX: 
@inproceedings{Khosrou2018,
  author = {Samaneh Khoshrou and Mykola Pechenizkiy},
  title = {MDL-based Development of Ensembles with Active Learning over Evolving Data Streams},
  booktitle = {Proceedings of 4th Workshop on Mining and Learning from Time Series (Milet's) @ KDD 2018},
  year = {2018},
  file = {Khoshrou2018.pdf:https//milets18.github.io/papers/milets18_paper_16.pdf:PDF}
}
journal Luna, J.M., Pechenizkiy, M., del Jesus, M.J. & Ventura, S. (2018) Mining context-aware association rules using grammar-based genetic programming, IEEE Transactions on Cybernetics, 48(11), Institute of Electrical and Electronics Engineers, pp. 3030-3044.

Abstract: Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.
BibTeX: 
@article{Luna2018,
  author = {J.M. Luna and M. Pechenizkiy and del Jesus, M.J. and S. Ventura},
  title = {Mining context-aware association rules using grammar-based genetic programming},
  journal = {IEEE Transactions on Cybernetics},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2018},
  volume = {48},
  number = {11},
  pages = {3030--3044},
  doi = {http://doi.org/10.1109/TCYB.2017.2750919},
  file = {Luna2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Luna2018.pdf:PDF}
}
special issue Yaman, A., Iacca, G., Coler, M., Fletcher, G.H.L. & Pechenizkiy, M. (2018) Multi-strategy differential evolution, In Applications of Evolutionary Computation, LNCS, Springer, pp. 617-633.

Abstract: We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art
BibTeX: 
@inproceedings{Yaman2018a,
  author = {A. Yaman and G. Iacca and M. Coler and G.H.L. Fletcher and M. Pechenizkiy},
  title = {Multi-strategy differential evolution},
  booktitle = {Applications of Evolutionary Computation},
  editor = {Kevin Sim and Paul Kaufmann},
  publisher = {Springer},
  year = {2018},
  pages = {617--633},
  doi = {http://doi.org/10.1007/978-3-319-77538-8_42},
  file = {Yaman2018a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Yaman2018a.pdf:PDF}
}
conference Ligtenberg, W., Pei, Y., Fletcher, G.H.L. & Pechenizkiy, M. (2018) Tink: a temporal graph analytics library for Apache Flink, In Prooceedings of 27th International Conference on World Wide Web (WWW), ACM, pp. 71-72.

Abstract: We introduce the Tink library for distributed temporal graph analytics. Increasingly, reasoning about temporal aspects of graph-structured data collections is an important aspect of analytics. For example, in a communication network, time plays a fundamental role in the propagation of information within the network. Whereas existing tools for temporal graph analysis are built stand alone, Tink is a library in the Apache Flink ecosystem, thereby leveraging its advanced mature features such as distributed processing and query optimization. Furthermore, Flink requires little effort to process and clean the data without having to use different tools before analyzing the data. Tink focuses on interval graphs in which every edge is associated with a starting time and an ending time. The library provides facilities for temporal graph creation and maintenance, as well as standard temporal graph measures and algorithms. Furthermore, the library is designed for ease of use and extensibility.
BibTeX: 
@inproceedings{Fletcher2018,
  author = {Wouter Ligtenberg and Yulong Pei and George H. L. Fletcher and Mykola Pechenizkiy},
  title = {Tink: a temporal graph analytics library for Apache Flink},
  booktitle = {Prooceedings of 27th International Conference on World Wide Web (WWW)},
  publisher = {ACM},
  year = {2018},
  pages = {71--72},
  doi = {http://doi.org/10.1145/3184558.3186934},
  file = {Ligtenberg2018.pdf:https//dl.acm.org/doi/pdf/10.1145/3184558.3186934:PDF}
}
journal Sicilia, R., Lo Giudice, S., Pei, Y., Pechenizkiy, M. & Soda, P. (2018) Twitter rumour detection in the health domain, Expert Systems with Applications, 110, Elsevier, pp. 33-40.

Abstract: In the last years social networks have emerged as a critical mean for information spreading bringing along several advantages. At the same time, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. For this reason, although the identification in social microblogs of which topic is a rumour has been studied in several works, there is the need to detect if a post is either a rumor or not. In this paper we cope with this last challenge presenting a novel rumour detection system that leverages on newly designed features, including influence potential and network characteristics measures. We tested our approach on a real dataset composed of health-related posts collected from Twitter microblog. We observe promising results, as the system is able to correctly detect about 90% of rumours, with acceptable levels of precision.
BibTeX: 
@article{Sicilia2018,
  author = {Rosa Sicilia and Lo Giudice, Stella and Yulong Pei and Mykola Pechenizkiy and Paolo Soda},
  title = {Twitter rumour detection in the health domain},
  journal = {Expert Systems with Applications},
  publisher = {Elsevier},
  year = {2018},
  volume = {110},
  pages = {33--40},
  doi = {http://doi.org/10.1016/j.eswa.2018.05.019},
  file = {Sicilia2018.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Sicilia2018.pdf:PDF}
}
journal Costante, E., den Hartog, J., Petkovic, M., Etalle, S. & Pechenizkiy, M. (2017) A white-box anomaly-based framework for database leakage detection, Journal of Information Security and Applications, 32, Elsevier, pp. 27-46.

Abstract: Data leakage is at the heart most of the privacy breaches worldwide. In this paper we present a white-box approach to detect potential data leakage by spotting anomalies in database transactions. We refer to our solution as white-box because it builds self explanatory profiles that are easy to understand and update, as opposite to black-box systems which create profiles hard to interpret and maintain (e.g., neural networks). In this paper we introduce our approach and we demonstrate that it is a major leap forward w.r.t. previous work on the topic in several aspects: (i) it significantly decreases the number of false positives, which is orders of magnitude lower than in state-of-the-art comparable approaches (we demonstrate this using an experimental dataset consisting of millions of real enterprise transactions); (ii) it creates profiles that are easy to understand and update, and therefore it provides an explanation of the origins of an anomaly; (iii) it allows the introduction of a feedback mechanism that makes possible for the system to improve based on its own mistakes; and (iv) feature aggregation and transaction flow analysis allow the system to detect threats which span over multiple features and multiple transactions.
BibTeX: 
@article{Costante2017,
  author = {E. Costante and den Hartog, J. and M. Petkovic and S. Etalle and M. Pechenizkiy},
  title = {A white-box anomaly-based framework for database leakage detection},
  journal = {Journal of Information Security and Applications},
  publisher = {Elsevier},
  year = {2017},
  volume = {32},
  pages = {27--46},
  doi = {http://doi.org/10.1016/j.jisa.2016.10.001},
  file = {Costante2017.pdf:http//www.sciencedirect.com/science/article/pii/S2214212616302629:PDF}
}
edited volume Duivesteijn, W., Pechenizkiy, M., Fletcher, G.H.L., Menkovski, V., Postma, E.J., Vanschoren, J. & van der Putten, P. (Eds.) (2017) Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017.

BibTeX: 
@book{Duivesteijn2017a,,
  title = {Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017},
  year = {2017},
  file = {Duivesteijn2017a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Duivesteijn2017a.pdf:PDF}
}
conference Maslov, A., Pechenizkiy, M., Pei, Y., Zliobaite, I., Shklyaev, A., Karkkainen, T. & Hollmen, J. (2017) BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints, In Proceedings of 2017 International Joint Conference on Neural Networks (IJCNN), 14-19 May 2017, Anchorage, Arkansas, Institute of Electrical and Electronics Engineers, pp. 1916-1923.

Abstract: Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance of popular Bayesian online detectors can be improved in case of recurrent changes. Modelling recurrence allows us to anticipate future changepoints and predict their locations in time. We propose an approach for inducing and integrating recurrence information in the streaming settings, and demonstrate its effectiveness on synthetic and real-world human activity datasets.
BibTeX: 
@inproceedings{Maslov2017,
  author = {A. Maslov and M. Pechenizkiy and Y. Pei and I. Zliobaite and A. Shklyaev and T. Karkkainen and J. Hollmen},
  title = {BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints},
  booktitle = {Proceedings of 2017 International Joint Conference on Neural Networks (IJCNN), 14-19 May 2017, Anchorage, Arkansas},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2017},
  pages = {1916--1923},
  doi = {http://doi.org/10.1109/IJCNN.2017.7966085},
  file = {Maslov2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Maslov2017.pdf:PDF}
}
workshop van der Zon, S.B., Zeev Ben Mordehay, O., Vrijdag, T.S., van Ipenburg, W., Veldsink, J., Duivesteijn, W. & Pechenizkiy, M. (2017) BoostEMM : Transparent boosting using exceptional model mining, In Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia, CEUR Workshop Proceedings, CEUR-WS.org, pp. 5-16.

Abstract: Boosting is an iterative ensemble-learning paradigm. Every iteration, a weak predictor learns a classification task, taking into account performance achieved in previous iterations. This is done by assigning weights to individual records of the dataset, which are increased if the record is misclassified by the previous weak predictor. Hence, subsequent predictors learn to focus on problematic records in the dataset. Boosting ensembles such as AdaBoost have shown to be effective models at fighting both high variance and high bias, even in challenging situations such as class imbalance. However, some aspects of AdaBoost might imply limitations for its deployment in the real world. On the one hand, focusing on problematic records can lead to overfitting in the presence of random noise. On the other hand, learning a boosting ensemble that assigns higher weights to hard-to-classify people might throw up serious questions in the age of responsible and transparent data analytics; if a bank must tell a customer that they are denied a loan, because the underlying algorithm made a decision specifically focusing the customer since they are hard to classify, this could be legally dubious. To kill these two birds with one stone, we introduce BoostEMM: a variant of AdaBoost where in every iteration of the procedure, rather than boosting problematic records, we boost problematic subgroups as found through Exceptional Model Mining. Boosted records being part of a coherent group should prevent overfitting, and explicit definitions of the subgroups of people being boosted enhances the transparency of the algorithm.
BibTeX: 
@inproceedings{vanderZon2017,
  author = {van der Zon, S.B. and Zeev Ben Mordehay, O. and T.S. Vrijdag and van Ipenburg, W. and J. Veldsink and W. Duivesteijn and M. Pechenizkiy},
  title = {BoostEMM : Transparent boosting using exceptional model mining},
  booktitle = {Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia},
  editor = {I. Bordino and G. Caldarelli and F. Fumarola and F. Gullo and T. Squartini},
  publisher = {CEUR-WS.org},
  year = {2017},
  pages = {5--16},
  file = {vanderZon2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/vanderZon2017.pdf:PDF}
}
conference Ahmadi, N., Pei, Y. & Pechenizkiy, M. (2017) Detection of alcoholism based on EEG signals and functional brain network features extraction, In Proceedings of IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2017, Thessaloniki, Greece, Institute of Electrical and Electronics Engineers, pp. 179-184.

Abstract: Alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioral impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analysing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyse the EEG signals for two groups of alcoholic and control subjects. Then by applying wavelet transform, band-limited EEG signals are decomposed into five frequency sub-bands. Also, the principle component analysis (PCA) is employed to choose the most information carrying channels. By examining various features from different frequency sub-bands, six discriminative features for classification are selected. From functional brain network perspective, the lower synchronization in Beta frequency sub-band and loss of lateralization in Alpha frequency sub-band in alcoholic subjects are observed. Also from signal processing perspective we found that alcoholic subjects have lower values of fractal dimension, energy and entropy compared to control ones. Five different classifiers are used to classify these groups of alcoholic and control subjects that show very high accuracies (more than 90%). However, by comparing the performance of different classifiers, SVM, random forest and gradient boosting show the best performances with accuracies near 100%. Our study shows that fractal dimension, entropy and energy of channel C1 in Alpha frequency sub-band are the more important features for classification.
BibTeX: 
@inproceedings{Ahmadi2017,
  author = {N. Ahmadi and Y. Pei and M. Pechenizkiy},
  title = {Detection of alcoholism based on EEG signals and functional brain network features extraction},
  booktitle = {Proceedings of IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2017, Thessaloniki, Greece},
  editor = {Bamidis, Panagiotis D. and Konstantinidis, Stathis Th. and Rodrigues, Pedro Pereira},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2017},
  pages = {179--184},
  doi = {http://doi.org/10.1109/CBMS.2017.46},
  file = {Ahmadi2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Ahmadi2017.pdf:PDF}
}
conference Duivesteijn, W., Farzami, T., Putman, T., Peer, E., Weerts, H.J.P., Adegeest, J.N., Foks, G. & Pechenizkiy, M. (2017) Have it both ways: from A/B testing to A&B testing with exceptional model mining, In Proceedings of European Confernece on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2017), Lecture Notes in Computer Science, Springer, pp. 114-126.

Abstract: In traditional A/B testing, we have two variants of the same product, a pool of test subjects, and a measure of success. In a randomized experiment, each test subject is presented with one of the two variants, and the measure of success is aggregated per variant. The variant of the product associated with the most success is retained, while the other variant is discarded. This, however, presumes that the company producing the products only has enough capacity to maintain one of the two product variants. If more capacity is available, then advanced data science techniques can extract more profit for the company from the A/B testing results. Exceptional Model Mining is one such advanced data science technique, which specializes in identifying subgroups that behave differently from the overall population. Using the association model class for EMM, we can find subpopulations that prefer variant A where the general population prefers variant B, and vice versa. This data science technique is applied on data from StudyPortals, a global study choice platform that ran an A/B test on the design of aspects of their website.
BibTeX: 
@inproceedings{Duivesteijn2017,
  author = {Wouter Duivesteijn and Tara Farzami and Thijs Putman and Evertjan Peer and Weerts, Hilde J.P. and Adegeest, Jasper N. and Gerson Foks and Mykola Pechenizkiy},
  title = {Have it both ways: from A/B testing to A&B testing with exceptional model mining},
  booktitle = {Proceedings of European Confernece on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2017)},
  editor = {Michelangelo Ceci and Saso Dzeroski and Donato Malerba and Yasemin Altun and Kamalika Das and Jesse Read and Marinka Zitnik and Jerzy Stefanowski and Taneli Mielikäinen},
  publisher = {Springer},
  year = {2017},
  pages = {114--126},
  doi = {http://doi.org/10.1007/978-3-319-71273-4_10},
  file = {Duivesteijn2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Duivesteijn2017.pdf:PDF}
}
conference Sicilia, R., Giudice, S.L., Pei, Y., Pechenizkiy, M. & Soda, P. (2017) Health-related rumour detection on Twitter, In Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, 2017-January, Institute of Electrical and Electronics Engineers, pp. 1599-1606.

Abstract: In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision.
BibTeX: 
@inproceedings{Sicilia2017,
  author = {Rosa Sicilia and Giudice, Stella Lo and Yulong Pei and Mykola Pechenizkiy and Paolo Soda},
  title = {Health-related rumour detection on Twitter},
  booktitle = {Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2017},
  volume = {2017-January},
  pages = {1599--1606},
  doi = {http://doi.org/10.1109/BIBM.2017.8217899},
  file = {Sicilia2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Sicilia2017.pdf:PDF}
}
workshop Martinez-Maldonado, R., Echeverria, V., Yacef, K., Dos Santos, A.D.P. & Pechenizkiy, M. (2017) How to capitalise on mobility, proximity and motion analytics to support formal and informal education?, In MMLA-CrossLAK 2017, CEUR Workshop Proceedings, CEUR-WS.org, pp. 39-46.

Abstract: Learning Analytics and similar data-intensive approaches aimed at understanding and/or supporting learning have mostly focused on the analysis of students' data automatically captured by personal computers or, more recently, mobile devices. Thus, most student behavioural data are limited to the interactions between students and particular learning applications. However, learning can also occur beyond these interface interactions, for instance while students interact face-to-face with other students or their teachers. Alternatively, some learning tasks may require students to interact with non-digital physical tools, to use the physical space, or to learn in different ways that cannot be mediated by traditional user interfaces (e.g. motor and/or audio learning). The key questions here are: why are we neglecting these kinds of learning activities? How can we provide automated support or feedback to students during these activities? Can we find useful patterns of activity in these physical settings as we have been doing with computer-mediated settings? This position paper is aimed at motivating discussion through a series of questions that can justify the importance of designing technological innovations for physical learning settings where mobility, proximity and motion are tracked, just as digital interactions have been so far.
BibTeX: 
@inproceedings{MartinezMaldonado2017b,
  author = {R. Martinez-Maldonado and V. Echeverria and K. Yacef and Dos Santos, A.D.P. and M. Pechenizkiy},
  title = {How to capitalise on mobility, proximity and motion analytics to support formal and informal education?},
  booktitle = {MMLA-CrossLAK 2017},
  publisher = {CEUR-WS.org},
  year = {2017},
  pages = {39--46},
  file = {MartinezMaldonado2017a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MartinezMaldonado2017a.pdf:PDF}
}
conference Martinez-Maldonado, R., Buckingham-Shum, S., Pechenizkiy, M., Power, T., Hayes, C. & Axisa, C. (2017) Modelling embodied mobility teamwork strategies in a simulation-based healthcare classroom, In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization UMAP '17 , 9-12 July 2017, Bratislava, Slovakia, ACM, pp. 308-312.

Abstract: In many situations, it remains critical for team members to develop strategies to effectively use the space and tools available to complete demanding tasks. However, despite the availability of sensors and analytics for instrumenting physical space, relatively little progress has been made in modelling the embodied dimensions of co-located teamwork. This paper explores an in-The-wild pilot study through which we explore a methodology to model embodied mobility teamwork strategies in the context of healthcare education. We developed the means for tracking, clustering and processing student-nurses' mobility data around a patient manikin. We illustrate the feasibility of our approach by discussing ways to make sense of these data to uncover meaningful trends, and the inherent challenges of applying physical space analytics in authentic settings.
BibTeX: 
@inproceedings{MartinezMaldonado2017a,
  author = {R. Martinez-Maldonado and S. Buckingham-Shum and M. Pechenizkiy and T. Power and C. Hayes and C. Axisa},
  title = {Modelling embodied mobility teamwork strategies in a simulation-based healthcare classroom},
  booktitle = {Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization UMAP '17 , 9-12 July 2017, Bratislava, Slovakia},
  publisher = {ACM},
  year = {2017},
  pages = {308--312},
  doi = {http://doi.org/10.1145/3079628.3079697},
  file = {MartinezMaldonado2017a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MartinezMaldonado2017a.pdf:PDF}
}
conference Zeev Ben Mordehay, O., Duivesteijn, W. & Pechenizkiy, M. (2017) "The nutcracker framework for ensemble interpretability", In 16th International Symposium on Intelligent Data Analysis (IDA 2017).

Abstract: The basic principles behind ensembles (e.g.Random Forest, AdaBoost) are simple. But we’re stillin trouble when attempting to explain the logic taken.Where does the problem lie? The reason that ensemblesare effective is that the base estimators "worktogether" and compensate each for the others’shortcomings.The Nutcracker Framework Given a trained ensembleand the relevant training / test dataset, construct predictionmatrix, M, cases (rows) against predictions (columns).Bicluster M to a given number of R x C biclusters.Now, investigate performance per bicluster (R x C).Identify feature importance per base estimators group (C).Describe each of the R cases subgroups in terms offeatures and values. We use Exceptional Model Mining for that task.Performance of the ensemble against the datasetcompared to performance of base estimator groupsagainst subgroups of cases, adds transparency.
BibTeX: 
@conference{ZeevBenMordehay2017,
  author = {Zeev Ben Mordehay, O. and W. Duivesteijn and M. Pechenizkiy},
  title = {The nutcracker framework for ensemble interpretability},
  booktitle = {16th International Symposium on Intelligent Data Analysis (IDA 2017)},
  year = {2017},
  note = {16th International Symposium on Intelligent Data Analysis (IDA 2017), October 26-28, 2017, London, UK, IDA 2017 ; Conference date: 26-10-2017 Through 28-10-2017},
  url = {http://www.dcs.bbk.ac.uk/ida2017/},
  pdf = {ZeevBenMordehay2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZeevBenMordehay2017.pdf:PDF}  
}
conference Martinez-Maldonado, R., Yacef, K., Dos Santos, A.D.P., Buckingham-Shum, S., Echeverria, V., Santos, O.C. & Pechenizkiy, M. (2017) Towards proximity tracking and sensemaking for supporting teamwork and learning, In IEEE 17th International Conference on Advanced Learning Technologies (ICALT 2017), pp. 89-91.

Abstract: A large number of learning tools offering some sort of personalisation features rely mainly on the analysis of logged interactions between students and particular user interfaces. Much less attention has been given to the analysis of physical aspects so often present in 'traditional' intellectual tasks, although these are both important in the full development of a life-long learner. This paper (1) discusses existing literature focused on supporting learning using proximity and location analytics and sensors, and, based on this, (2) illustrates the feasibility and potential of these analytics for teaching and learning through an study in the context of proximity and location analytics in a team-based health simulation classroom.
BibTeX: 
@inproceedings{MartinezMaldonado2017,
  author = {R. Martinez-Maldonado and K. Yacef and Dos Santos, A.D.P. and S. Buckingham-Shum and V. Echeverria and O.C. Santos and M. Pechenizkiy},
  title = {Towards proximity tracking and sensemaking for supporting teamwork and learning},
  booktitle = {IEEE 17th International Conference on Advanced Learning Technologies (ICALT 2017)},
  year = {2017},
  pages = {89--91},
  doi = {http://doi.org/10.1109/ICALT.2017.79},
  file = {MartinezMaldonado2017.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MartinezMaldonado2017.pdf:PDF}
}
conference Zhang, J., Pechenizkiy, M., Pei, Y. & Efremova, J. (2016) A Robust Density-based Clustering Algorithm for Multi-Manifold Structure, In Proceedings of The 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016), DM Track, ACM Press, pp. 832-838.

BibTeX: 
@inproceedings{ZhangSAC2016,
  author = {Jianpeng Zhang and Mykola Pechenizkiy and Yulong Pei and Julia Efremova},
  title = {A Robust Density-based Clustering Algorithm for Multi-Manifold Structure},
  booktitle = {Proceedings of The 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016), DM Track},
  publisher = {ACM Press},
  year = {2016},
  pages = {832--838},
  doi = {http://doi.org/10.1145/2851613.2851644},
  file = {ZhangSAC2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZhangSAC2016.pdf:PDF}
}
journal Onofri, L., Soda, P., Pechenizkiy, M. & Iannello, G. (2016) A survey on using domain and contextual knowledge for human activity recognition in video streams, Expert Systems with Applications, 63, pp. 97-111.

BibTeX: 
@article{Onofri2016,
  author = {Leonardo Onofri and Paolo Soda and Mykola Pechenizkiy and Giulio Iannello},
  title = {A survey on using domain and contextual knowledge for human activity recognition in video streams},
  journal = {Expert Systems with Applications},
  year = {2016},
  volume = {63},
  pages = {97--111},
  url = {http://www.sciencedirect.com/science/article/pii/S0957417416302913},
  doi = {http://doi.org/10.1016/j.eswa.2016.06.011},
  file = {Onofri2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Onofri2016.pdf:PDF}
}
book chapter Žliobaitė, I., Pechenizkiy, M. & Gama, J. (2016) "An overview of concept drift applications", In Big Data Analysis: New Algorithms for a New Society, Springer, pp. 91-114.

BibTeX: 
@incollection{zliobaite2015appsCD,
  author = {Indrė Žliobaitė and Mykola Pechenizkiy and Joao Gama},
  title = {An overview of concept drift applications},
  booktitle = {Big Data Analysis: New Algorithms for a New Society},
  publisher = {Springer},
  year = {2016},
  pages = {91-114},
  pdf = {ZliobaiteCDApps2015.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZliobaiteCDApps2015.pdf:PDF}  
}
conference Ahmadi, N. & Pechenizkiy, M. (2016) Application of Horizontal Visibility Graph as a Robust Measure of Neurophysiological Signals Synchrony, In 29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016, IEEE Computer Society, pp. 273-278.

BibTeX: 
@inproceedings{AhmadiP16,
  author = {Negar Ahmadi and Mykola Pechenizkiy},
  title = {Application of Horizontal Visibility Graph as a Robust Measure of Neurophysiological Signals Synchrony},
  booktitle = {29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016},
  publisher = {IEEE Computer Society},
  year = {2016},
  pages = {273--278},
  doi = {http://doi.org/10.1109/CBMS.2016.73},
  file = {AhmadiP16.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/AhmadiP16.pdf:PDF}
}
conference Maslov, A., Lam, H.T., Pechenizkiy, M., Bouillet, E. & Kärkkäinen, T. (2016) DOBRO: A Prediction Error Correcting Robot Under Drifts, In Proocedings of the 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016), DS Track, ACM Press, pp. 945-948.

BibTeX: 
@inproceedings{MaslovSAC2016,
  author = {Alexandr Maslov and Hoang Thanh Lam and Mykola Pechenizkiy and Eric Bouillet and Tommi Kärkkäinen},
  title = {DOBRO: A Prediction Error Correcting Robot Under Drifts},
  booktitle = {Proocedings of the 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016), DS Track},
  publisher = {ACM Press},
  year = {2016},
  pages = {945--948},
  doi = {http://doi.org/10.1145/2851613.2851888},
  file = {MaslovSAC2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MaslovSAC2016.pdf:PDF}
}
workshop Eggels, H., van Elk, R. & Pechenizkiy, M. (2016) Explaining soccer match outcomes with goal scoring opportunities predictive analytics, In Proceedings of the Workshop on Machine Learning and Data Mining for Sports Analytics 2016 @ ECML/PKDD 2016), CEUR Workshop Proceedings, CEUR-WS.org.

Abstract: In elite soccer, decisions are often based on recent results and emotions. In this paper, we propose a method to determine the expected winner of a match in elite soccer. The expected result of a soccer match is determined by estimating the probability of scoring for the individual goal scoring opportunities. The outcome of a match is then obtained by integrating these probabilities. In our experimental study, we show that the probabilities of goal scoring opportunities accurately match reality.
BibTeX: 
@inproceedings{Eggels2016,
  author = {H. Eggels and van Elk, R. and M. Pechenizkiy},
  title = {Explaining soccer match outcomes with goal scoring opportunities predictive analytics},
  booktitle = {Proceedings of the Workshop on Machine Learning and Data Mining for Sports Analytics 2016 @ ECML/PKDD 2016)},
  publisher = {CEUR-WS.org},
  year = {2016},
  file = {Eggels2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Eggels2016.pdf:PDF}
}
journal Tromp, E., Pechenizkiy, M. & Gaber, M. (2016) Expressive Modeling for Trusted Big Data Analytics: Techniques and Applications in Sentiment Analysis, Big Data Analytics, 2, pp. 5.

BibTeX: 
@article{Tromp,
  author = {Tromp, Erik and Pechenizkiy, Mykola and Gaber, Mohamed},
  title = {Expressive Modeling for Trusted Big Data Analytics: Techniques and Applications in Sentiment Analysis},
  journal = {Big Data Analytics},
  year = {2016},
  volume = {2},
  pages = {5},
  doi = {http://doi.org/10.1186/s41044-016-0018-9},
  file = {Tromp2016.pdf:https//link.springer.com/content/pdf/10.1186/s41044-016-0018-9.pdf:PDF}
}
demo Nieuwenhuijse, A., Bakker, J. & Pechenizkiy, M. (2016) Finding Incident-Related Social Media Messages for Emergency Awareness, In Proceeedings of European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), Lecture Notes in Computer Science, 9853, Springer, pp. 67-70.

BibTeX: 
@inproceedings{NieuwenhuijseBP16,
  author = {Alexander Nieuwenhuijse and Jorn Bakker and Mykola Pechenizkiy},
  title = {Finding Incident-Related Social Media Messages for Emergency Awareness},
  booktitle = {Proceeedings of European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)},
  publisher = {Springer},
  year = {2016},
  volume = {9853},
  pages = {67--70},
  doi = {http://doi.org/10.1007/978-3-319-46131-1_15},
  file = {NieuwenhuijseBP16.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/NieuwenhuijseBP16.pdf:PDF}
}
journal Luna, J.M., Pechenizkiy, M. & Ventura, S. (2016) Mining Exceptional Relationships with Grammar-Guided Genetic Programming, Knowledge and Information Systems, 47, pp. 571–594.

BibTeX: 
@article{Luna_KAIS2015,
  author = {Jose Maria Luna and Mykola Pechenizkiy and Sebastián Ventura},
  title = {Mining Exceptional Relationships with Grammar-Guided Genetic Programming},
  journal = {Knowledge and Information Systems},
  year = {2016},
  volume = {47},
  pages = {571–594},
  doi = {http://doi.org/10.1007/s10115-015-0859-y},
  file = {MERG3P_KAIS.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MERG3P_KAIS.pdf:PDF}
}
conference Maslov, A., Pechenizkiy, M., Žliobaitė, I. & Kärkkäinen, T. (2016) Modelling Recurrent Events for Improving Online Change Detection, In Proceedings of SIAM International Conference on Data Mining (SDM16), SIAM, pp. 549-557.

BibTeX: 
@inproceedings{MaslovSDM2016,
  author = {Alexandr Maslov and Mykola Pechenizkiy and Indrė Žliobaitė and Tommi Kärkkäinen},
  title = {Modelling Recurrent Events for Improving Online Change Detection},
  booktitle = {Proceedings of SIAM International Conference on Data Mining (SDM16)},
  publisher = {SIAM},
  year = {2016},
  pages = {549--557},
  doi = {http://doi.org/10.1137/1.9781611974348.62},
  file = {MaslovSDM2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MaslovSDM2016.pdf:PDF}
}
workshop Pei, Y., Zhang, J., Fletcher, G.H.L. & Pechenizkiy, M. (2016) Node Classification in Dynamic Social Networks, In Proceedings of AALTD 2016: 2nd ECMLPKDD International Workshop on Advanced Analytics and Learning on Temporal Data, pp. 54-93.

BibTeX: 
@inproceedings{pei2016node,
  author = {Pei, Yulong and Zhang, Jianpeng and Fletcher, George HL and Pechenizkiy, Mykola},
  title = {Node Classification in Dynamic Social Networks},
  booktitle = {Proceedings of AALTD 2016: 2nd ECMLPKDD International Workshop on Advanced Analytics and Learning on Temporal Data},
  year = {2016},
  pages = {54--93},
  file = {aaltd16_proc.pdf#page=62:https//aaltd16.irisa.fr/files/2016/09/aaltd16_proc.pdf#page=62:PDF}
}
conference van Heeswijk, W., Fletcher, G.H.L. & Pechenizkiy, M. (2016) On structure preserving sampling and approximate partitioning of graphs, In Proceedings of The 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016), DS Track, ACM Press.

BibTeX: 
@inproceedings{HeeswijkSAC2016,
  author = {Wouter van Heeswijk and George H. L. Fletcher and Mykola Pechenizkiy},
  title = {On structure preserving sampling and approximate partitioning of graphs},
  booktitle = {Proceedings of The 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016), DS Track},
  publisher = {ACM Press},
  year = {2016},
  doi = {http://doi.org/10.1145/2851613.2851650},
  file = {HeeswijkSAC2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/HeeswijkSAC2016.pdf:PDF}
}
journal Luna, J.M., Cano, A., Pechenizkiy, M. & Ventura, S. (2016) Speeding-Up Association Rule Mining With Inverted Index Compression, IEEE Transactions on Cybernetics.

BibTeX: 
@article{LunaC_2016,
  author = {José María Luna and Alberto Cano and Mykola Pechenizkiy and Sebastián Ventura},
  title = {Speeding-Up Association Rule Mining With Inverted Index Compression},
  journal = {IEEE Transactions on Cybernetics},
  year = {2016},
  file = {LunaCYB2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/LunaCYB2016.pdf:PDF}
}
conference Zhang, J., Fletcher, G.H.L., Pei, Y. & Pechenizkiy, M. (2016) Structural measures of clustering quality on graph samples, In Proceedings of 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Institute of Electrical and Electronics Engineers, pp. 345-348.

Abstract: Due to the growing presence of large-scale and streaming graphs such as social networks, graph sampling and clustering play an important role in many real-world applications. One key aspect of graph clustering is the evaluation of cluster quality. However, little attention has been paid to evaluation measures for clustering quality on samples of graphs. As first steps towards appropriate evaluation of clustering methods on sampled graphs, in this work we present two novel evaluation measures for graph clustering called δ-precision and δ-recall. These measures effectively reflect the match quality of the clusters in the sampled graph with respect to the ground-truth clusters in the original graph. We show in extensive experiments on various benchmarks that our proposed metrics are practical and effective for graph clustering evaluation.
BibTeX: 
@inproceedings{Zhang2016,
  author = {J. Zhang and G.H.L. Fletcher and Y. Pei and M. Pechenizkiy},
  title = {Structural measures of clustering quality on graph samples},
  booktitle = {Proceedings of 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  publisher = {Institute of Electrical and Electronics Engineers},
  year = {2016},
  pages = {345--348},
  doi = {http://doi.org/10.1109/ASONAM.2016.7752256},
  file = {Zhang2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Zhang2016.pdf:PDF}
}
demo Garcia, A.M., Stash, N., Fabri, M., Bra, P.D., Fletcher, G.H.L. & Pechenizkiy, M. (2016) WiBAF into a CMS: Personalization in Learning Environments Made Easy, In Late-breaking Results, Posters, Demos, Doctoral Consortium and Workshops Proceedings of the 24th ACM Conference on User Modeling, Adaptation and Personalisation (UMAP 2016).

BibTeX: 
@inproceedings{GarciaSFBFP16,
  author = {Alejandro Montes Garcia and Natalia Stash and Marc Fabri and Paul De Bra and George H. L. Fletcher and Mykola Pechenizkiy},
  title = {WiBAF into a CMS: Personalization in Learning Environments Made Easy},
  booktitle = {Late-breaking Results, Posters, Demos, Doctoral Consortium and Workshops Proceedings of the 24th ACM Conference on User Modeling, Adaptation and Personalisation (UMAP 2016)},
  year = {2016},
  file = {http://ceur-ws.org/Vol-1618/PALE10.pdf}
}
panel abstract Gasevic, D., Martin, T., Pardos, Z., Pechenizkiy, M., Stamper, J. & Zaiane, O. (2015) Ethics and Privacy in EDM, In Proceedings of the 8th International Conference on Educational Data Mining.

BibTeX: 
@inproceedings{ethicspanelEDM2015,
  author = {Dragan Gasevic and Taylor Martin and Zach Pardos and Mykola Pechenizkiy and John Stamper and Osmar Zaiane},
  title = {Ethics and Privacy in EDM},
  booktitle = {Proceedings of the 8th International Conference on Educational Data Mining},
  year = {2015},
  file = {abstract13.pdf:http//www.educationaldatamining.org/EDM2015/proceedings/abstract13.pdf:PDF}
}
panel abstract Baker, R., Brusilovsky, P., Gasevic, D., Heffernan, N.T., Pechenizkiy, M. & Wise, A. (2015) Grand Challenges for EDM and Related Research Areas, In Proceedings of the 8th International Conference on Educational Data Mining.

BibTeX: 
@inproceedings{grandchallengesEDM2015,
  author = {Ryan Baker and Peter Brusilovsky and Dragan Gasevic and Neil T. Heffernan and Mykola Pechenizkiy and Alyssa Wise},
  title = {Grand Challenges for EDM and Related Research Areas},
  booktitle = {Proceedings of the 8th International Conference on Educational Data Mining},
  year = {2015},
  file = {abstract15.pdf:http//www.educationaldatamining.org/EDM2015/proceedings/abstract15.pdf:PDF}
}
conference Aravanis, G., Pechenizkiy, M. & Bucur, A. (2015) Hippocrates: A Context-aware, Collaboration Enabling Search Tool, In Proceedings of 28th IEEE Conference on Computer-Based Medical Systems (CBMS 2015), IEEE Computer Society, pp. 320-325.

BibTeX: 
@inproceedings{aravanis2015hippocrates,
  author = {Georgios Aravanis and Mykola Pechenizkiy and Anca Bucur},
  title = {Hippocrates: A Context-aware, Collaboration Enabling Search Tool},
  booktitle = {Proceedings of 28th IEEE Conference on Computer-Based Medical Systems (CBMS 2015)},
  publisher = {IEEE Computer Society},
  year = {2015},
  pages = {320--325},
  file = {AravanisCBMS2015.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/AravanisCBMS2015.pdf:PDF}
}
special issue Pechenizkiy, M. & Gasevic, D. (2015) Introduction into Sparks of the Learning Analytics Future, Journal of Learning Analytics, 1(3), pp. 145-149.

BibTeX: 
@article{pechenizkiy2015introduction,
  author = {Pechenizkiy, Mykola and Gasevic, Dragan},
  title = {Introduction into Sparks of the Learning Analytics Future},
  journal = {Journal of Learning Analytics},
  year = {2015},
  volume = {1},
  number = {3},
  pages = {145--149},
  file = {4422:http//epress.lib.uts.edu.au/journals/index.php/JLA/article/viewFile/4318/4422:PDF}
}
book chapter Tromp, E. & Pechenizkiy, M. (2015) "Pattern-Based Emotion Classification on Social Media", In Advances in Social Media Analysis, Vol. 602, Springer, pp. 1-20.

BibTeX: 
@incollection{TrompP15,
  author = {Erik Tromp and Mykola Pechenizkiy},
  title = {Pattern-Based Emotion Classification on Social Media},
  booktitle = {Advances in Social Media Analysis},
  publisher = {Springer},
  year = {2015},
  volume = {602},
  pages = {1--20},
  doi = {http://doi.org/10.1007/978-3-319-18458-6_1}  
}
invited talk abstract Pechenizkiy, M. (2015) Predictive Analytics on Evolving Data Streams: Anticipating and Adapting to Changes in Known and Unknown Contexts, In Proceedings of the 13th Int. Conf. on High Performance Computing and Simulation (HPCS 2015).

BibTeX: 
@inproceedings{paeds_HPCS2015,
  author = {Mykola Pechenizkiy},
  title = {Predictive Analytics on Evolving Data Streams: Anticipating and Adapting to Changes in Known and Unknown Contexts},
  booktitle = {Proceedings of the 13th Int. Conf. on High Performance Computing and Simulation (HPCS 2015)},
  year = {2015},
  file = {PA_EDS_HPCS15.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PA_EDS_HPCS15.pdf:PDF}
}
proceedings Santos, O.C., Boticario, J.G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J.M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S. & Desmarais, M. (2015) "Proceedings of the 8th International Conference on Educational Data Mining".

BibTeX: 
@proceedings{EDM2015proceedings,
  author = {Olga C. Santos and Jesus G. Boticario and Cristobal Romero and Mykola Pechenizkiy and Agathe Merceron and Piotr Mitros and José María Luna and Cristian Mihaescu and Pablo Moreno and Arnon Hershkovitz and Sebastian Ventura and Michel Desmarais},
  title = {Proceedings of the 8th International Conference on Educational Data Mining},
  year = {2015},
  url = {http://educationaldatamining.org/EDM2015/proceedings/},
  pdf = {edm2015_proceedings.pdf:http//www.educationaldatamining.org/EDM2015/proceedings/edm2015_proceedings.pdf:PDF}  
}
conference Chernov, S., Pechenizkiy, M. & Ristaniemi, T. (2015) The influence of dataset size on the performance of cell outage detection approach in LTE-A networks, In Proceedings of 10th International Conference on Information, Communications and Signal Processing, ICICS 2015, pp. 1-5.

BibTeX: 
@inproceedings{ChernovPR15,
  author = {Sergey Chernov and Mykola Pechenizkiy and Tapani Ristaniemi},
  title = {The influence of dataset size on the performance of cell outage detection approach in LTE-A networks},
  booktitle = {Proceedings of 10th International Conference on Information, Communications and Signal Processing, ICICS 2015},
  year = {2015},
  pages = {1--5},
  doi = {http://doi.org/10.1109/ICICS.2015.7459819},
  file = {Chernov2016.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Chernov2016.pdf:PDF}
}
poster Montes Garcia, A., De Bra, P., Fletcher, G. & Pechenizkiy, M. (2014) A DSL Based on CSS for Hypertext Adaptation, In Proceedings of 25nd ACM Conference on Hypertext and Hypermedia (HT '2014), ACM Press.

BibTeX: 
@inproceedings{MontesHT2014,
  author = {Montes Garcia, Alejandro and De Bra, Paul and Fletcher, George and Mykola Pechenizkiy},
  title = {A DSL Based on CSS for Hypertext Adaptation},
  booktitle = {Proceedings of 25nd ACM Conference on Hypertext and Hypermedia (HT '2014)},
  publisher = {ACM Press},
  year = {2014},
  file = {MontesHT2014.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MontesHT2014.pdf:PDF}
}
journal Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M. & Bouchachia, A. (2014) A Survey on Concept Drift Adaptation, ACM Computing Surveys, 46(4), ACM, pp. 44:1-44:37.

BibTeX: 
@article{GamaACMCS2014,
  author = {Joao Gama and Indrė Žliobaitė and Albert Bifet and Mykola Pechenizkiy and Abdelhamid Bouchachia},
  title = {A Survey on Concept Drift Adaptation},
  journal = {ACM Computing Surveys},
  publisher = {ACM},
  year = {2014},
  volume = {46},
  number = {4},
  pages = {44:1--44:37},
  url = {http://doi.acm.org/10.1145/2523813},
  doi = {http://doi.org/10.1145/2523813},
  file = {Gama_ACMCS_AdaptationCD_accepted.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Gama_ACMCS_AdaptationCD_accepted.pdf:PDF}
}
conference Kiseleva, J., Garc\ia, A.M., Luo, Y., Pechenizkiy, M., Bra, P.D. & Kamps, J. (2014) Applying Learning to Rank Techniques to Contextual Suggestions, In Proceedings of The Twenty-Third Text REtrieval Conference, TREC 2014, Gaithersburg, Maryland, USA, November 19-21, 2014.

BibTeX: 
@inproceedings{DBLP:conf/trec/KiselevaGLPBK14,
  author = {Julia Kiseleva and Alejandro Montes Garc\ia and Yongming Luo and Mykola Pechenizkiy and Paul De Bra and Jaap Kamps},
  title = {Applying Learning to Rank Techniques to Contextual Suggestions},
  booktitle = {Proceedings of The Twenty-Third Text REtrieval Conference, TREC 2014, Gaithersburg, Maryland, USA, November 19-21, 2014},
  editor = {Ellen M. Voorhees and Angela Ellis},
  year = {2014},
  file = {pro-eindhoven_cs.pdf:http//trec.nist.gov/pubs/trec23/papers/pro-eindhoven_cs.pdf:PDF}
}
journal Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P., Zliobaite, I. & Pechenizkiy, M. (2014) Dealing with concept drifts in process mining, IEEE Transactions on Neural Networks and Learning Systems, 25(1), pp. 154-171.

BibTeX: 
@article{BoseJC_TNNLS2013,
  author = {R.P. Jagadeesh Chandra Bose and W.M.P. van der Aalst and Indre Zliobaite and Mykola Pechenizkiy},
  title = {Dealing with concept drifts in process mining},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  year = {2014},
  volume = {25},
  number = {1},
  pages = {154-171},
  file = {JCBose_TNNLS.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/JCBose_TNNLS.pdf:PDF}
}
workshop Lam, H.T., Kiseleva, J., Pechenizkiy, M. & Calders, T. (2014) Decomposing a Sequence into Independent Subsequences Using Compression Algorithms, In Proceedings of the SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA 2014), pp. 67-74.

BibTeX: 
@inproceedings{LamIDEA2014,
  author = {Hoang Thanh Lam and Julia Kiseleva and Mykola Pechenizkiy and Toon Calders},
  title = {Decomposing a Sequence into Independent Subsequences Using Compression Algorithms},
  booktitle = {Proceedings of the SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA 2014)},
  year = {2014},
  pages = {67-74},
  file = {LamIDEA2014.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/LamIDEA2014.pdf:PDF}
}
conference Costante, E., den Hartog, J., Petkovic, M., Etalle, S. & Pechenizkiy, M. (2014) Hunting the Unknown - White-Box Database Leakage Detection, In 28th Conf. on Data and Applications Security and Privacy (DBSec 2014), Lecture Notes in Computer Science, 8566, Springer, pp. 243-259.

BibTeX: 
@inproceedings{CostanteHPEP14,
  author = {Elisa Costante and Jerry den Hartog and Milan Petkovic and Sandro Etalle and Mykola Pechenizkiy},
  title = {Hunting the Unknown - White-Box Database Leakage Detection},
  booktitle = {28th Conf. on Data and Applications Security and Privacy (DBSec 2014)},
  editor = {Vijay Atluri and Günther Pernul},
  publisher = {Springer},
  year = {2014},
  volume = {8566},
  pages = {243-259},
  doi = {http://doi.org/10.1007/978-3-662-43936-4_16},
  file = {costanteDBSec14.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/costanteDBSec14.pdf:PDF}
}
poster Pechenizkiy, M. & Toledo, P.A. (2014) Learning to Teach like a Bandit, In 7th International Conference on Educational Data Mining (EDM 2014).

BibTeX: 
@inproceedings{pechenizkiy2014learning,
  author = {Pechenizkiy, Mykola and Toledo, Pedro A},
  title = {Learning to Teach like a Bandit},
  booktitle = {7th International Conference on Educational Data Mining (EDM 2014)},
  year = {2014},
  file = {57_EDM-2014-Poster.pdf:http//educationaldatamining.org/EDM2014/uploads/procs2014/posters/57_EDM-2014-Poster.pdf:PDF}
}
technical report Tromp, E. & Pechenizkiy, M. (2014) Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel CoRR, arXiv: 1412.4682.

BibTeX: 
@techreport{DBLP:journals/corr/TrompP14,
  author = {Erik Tromp and Mykola Pechenizkiy},
  title = {Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel},
  school = {CoRR},
  year = {2014},
  number = {arXiv: 1412.4682},
  url = {http://arxiv.org/abs/1412.4682},
  file = {1412.4682v1.pdf:http//arxiv.org/pdf/1412.4682v1.pdf:PDF}
}
technical report Williams, J.J., Li, N., Kim, J., Whitehill, J., Maldonado, S., Pechenizkiy, M., Chu, L. & Heffernan, N. (2014) The MOOClet Framework: Improving Online Education through Experimentation and Personalization of Modules.

BibTeX: 
@techreport{williams2014mooclet,
  author = {Williams, Joseph Jay and Li, Na and Kim, Juho and Whitehill, Jacob and Maldonado, Samuel and Pechenizkiy, Mykola and Chu, Larry and Heffernan, Neil},
  title = {The MOOClet Framework: Improving Online Education through Experimentation and Personalization of Modules},
  year = {2014},
  doi = {http://doi.org/10.2139/ssrn.2523265},
  file = {abstract=2523265:http//ssrn.com/abstract=2523265:PDF}
}
poster Kurniawan, H., Maslov, A. & Pechenizkiy, M. (2014) Towards the Stress Analytics Framework: Managing, Mining and Visualizing Multi-Modal Data for Stress Awareness, In Proceedings of 27th IEEE Conference on Computer-Based Medical Systems (CBMS 2014), IEEE Computer Society.

BibTeX: 
@inproceedings{KurniawanCBMS2014,
  author = {Hindra Kurniawan and Alexandr Maslov and Mykola Pechenizkiy},
  title = {Towards the Stress Analytics Framework: Managing, Mining and Visualizing Multi-Modal Data for Stress Awareness},
  booktitle = {Proceedings of 27th IEEE Conference on Computer-Based Medical Systems (CBMS 2014)},
  publisher = {IEEE Computer Society},
  year = {2014},
  file = {KurniawanCBMS2014.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KurniawanCBMS2014.pdf:PDF}
}
journal Song, M., Yang, H., Siadat, S.H. & Pechenizkiy, M. (2013) A comparative study of dimensionality reduction techniques to enhance trace clustering performances, Expert Systems with Applications, 40, pp. in press.

BibTeX: 
@article{Song2013,
  author = {M. Song and Hanna Yang and S.H. Siadat and M. Pechenizkiy},
  title = {A comparative study of dimensionality reduction techniques to enhance trace clustering performances},
  journal = {Expert Systems with Applications},
  year = {2013},
  volume = {40},
  pages = {in press},
  url = {http://www.sciencedirect.com/science/article/pii/S095741741201319X},
  doi = {http://doi.org/10.1016/j.eswa.2012.12.078},
  file = {SongESA2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/SongESA2013.pdf:PDF}
}
conference Marrero, A., Mendez, J.A., Maslov, A. & Pechenizkiy, M. (2013) ACLAC: An approach for adaptive closed-loop anesthesia control, In Proceedings of 26th IEEE Conference on Computer-Based Medical Systems (CBMS 2013), IEEE Computer Society, pp. 285-290.

BibTeX: 
@inproceedings{MarreroCBMS2013,
  author = {Ayoze Marrero and Juan A. Mendez and Alexandr Maslov and Mykola Pechenizkiy},
  title = {ACLAC: An approach for adaptive closed-loop anesthesia control},
  booktitle = {Proceedings of 26th IEEE Conference on Computer-Based Medical Systems (CBMS 2013)},
  publisher = {IEEE Computer Society},
  year = {2013},
  pages = {285-290},
  doi = {http://doi.org/10.1109/CBMS.2013.6627803},
  file = {MarreroCBMS2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MarreroCBMS2013.pdf:PDF}
}
workshop Demirtas, E. & Pechenizkiy, M. (2013) Cross-lingual Polarity Detection with Machine Translation, In Proceedings of the Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM@KDD2013), ACM.

BibTeX: 
@inproceedings{DemirtasWISDOM2013,
  author = {Erkin Demirtas and Mykola Pechenizkiy},
  title = {Cross-lingual Polarity Detection with Machine Translation},
  booktitle = {Proceedings of the Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM@KDD2013)},
  publisher = {ACM},
  year = {2013},
  file = {MT_WISDOM2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MT_WISDOM2013.pdf:PDF}
}
technical report Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P., Žliobaitė, I. & Pechenizkiy, M. (2013) Dealing with concept drifts in process mining : a case study in a Dutch municipality BPM Center Report, BPMcenter.org, No. BPM-13-13.

BibTeX: 
@techreport{BoseTechReport2013,
  author = {R.P. Jagadeesh Chandra Bose and W.M.P. van der Aalst and Indrė Žliobaitė and Mykola Pechenizkiy},
  title = {Dealing with concept drifts in process mining : a case study in a Dutch municipality},
  school = {BPM Center Report, BPMcenter.org},
  year = {2013},
  number = {No. BPM-13-13},
  file = {900712427329104:http//purl.tue.nl/900712427329104:PDF}
}
workshop Kiseleva, J., Lam, H.T., Pechenizkiy, M. & Calders, T. (2013) Discovering temporal hidden contexts in web sessions for user trail prediction, In Proceedings of the 22nd international conference on World Wide Web, (Companion Volume, TempWeb@WWW'2013 ), ACM, pp. 1067-1074.

BibTeX: 
@inproceedings{KiselevaLPC_www13,
  author = {Julia Kiseleva and Hoang Thanh Lam and Mykola Pechenizkiy and Toon Calders},
  title = {Discovering temporal hidden contexts in web sessions for user trail prediction},
  booktitle = {Proceedings of the 22nd international conference on World Wide Web, (Companion Volume, TempWeb@WWW'2013 )},
  editor = {Leslie Carr and Alberto H. F. Laender and Bernadette Farias Lóscio and Irwin King and Marcus Fontoura and Denny Vrandecic and Lora Aroyo and José Palazzo M. de Oliveira and Fernanda Lima and Erik Wilde},
  publisher = {ACM},
  year = {2013},
  pages = {1067-1074},
  doi = {http://dl.acm.org/citation.cfm?id=2488120},
  file = {KiselevaTempWeb2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KiselevaTempWeb2013.pdf:PDF}
}
journal Zafra, A., Pechenizkiy, M. & Ventura, S. (2013) HyDR-MI: A Hybrid Algorithm to Reduce Dimensionality in Multiple Instance Learning, Information Sciences, 222, pp. 282-301.

BibTeX: 
@article{ZafraIS2013,
  author = {Amelia Zafra and Mykola Pechenizkiy and Sebastián Ventura},
  title = {HyDR-MI: A Hybrid Algorithm to Reduce Dimensionality in Multiple Instance Learning},
  journal = {Information Sciences},
  year = {2013},
  volume = {222},
  pages = {282-301},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0020025511000612},
  doi = {http://doi.org/10.1016/j.ins.2011.01.034},
  file = {ZafraIS2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZafraIS2013.pdf:PDF}
}
editorial Pechenizkiy, M. & Žliobaitė, I. (2013) Introduction to the special issue on handling concept drift in adaptive information systems, Evolving Systems, Springer-Verlag, pp. 1-2.

BibTeX: 
@article{PechenizkiyZliobaite2012,
  author = {Pechenizkiy, Mykola and Indre Žliobaitė},
  title = {Introduction to the special issue on handling concept drift in adaptive information systems},
  journal = {Evolving Systems},
  publisher = {Springer-Verlag},
  year = {2013},
  pages = {1-2},
  doi = {http://doi.org/10.1007/s12530-012-9070-5},
  file = {s12530-012-9070-5:http//dx.doi.org/10.1007/s12530-012-9070-5:PDF}
}
proceedings Pechenizkiy, M. & Wojciechowski, M. (2013) "New Trends in Databases and Information Systems, Workshop Proceedings of the 16th East European Conference, ADBIS 2012, Poznan, Poland, September 17-21, 2012", In ADBIS Workshops, Vol. 185, Springer.

BibTeX: 
@proceedings{ADBIS2012,
  author = {Mykola Pechenizkiy and Marek Wojciechowski},
  title = {New Trends in Databases and Information Systems, Workshop Proceedings of the 16th East European Conference, ADBIS 2012, Poznan, Poland, September 17-21, 2012},
  booktitle = {ADBIS Workshops},
  publisher = {Springer},
  year = {2013},
  volume = {185},
  doi = {http://doi.org/10.1007/978-3-642-32518-2}  
}
workshop Kiseleva, J., Lam, H.T., Pechenizkiy, M. & Calders, T. (2013) Predicting current user intent with contextual Markov models, In (submitted to) Proceedings of the Domain-Driven Data Mining Workshop @ ICDM'2013, (DDDM'2013), IEEE.

BibTeX: 
@inproceedings{KiselevaLPC_DDDM13,
  author = {Julia Kiseleva and Hoang Thanh Lam and Mykola Pechenizkiy and Toon Calders},
  title = {Predicting current user intent with contextual Markov models},
  booktitle = {(submitted to) Proceedings of the Domain-Driven Data Mining Workshop @ ICDM'2013, (DDDM'2013)},
  publisher = {IEEE},
  year = {2013},
  file = {KiselevaDS2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KiselevaDS2013.pdf:PDF}
}
journal Ang, H.H., Gopalkrishnan, V., Žliobaitė, I., Pechenizkiy, M. & Hoi, S.C.H. (2013) Predictive Handling of Asynchronous Concept Drifts in Distributed Environments, IEEE Transactions on Knowledge and Data Engineering, 25(10), pp. 2343-2355.

BibTeX: 
@article{Ang2013,
  author = {Ang, H.H. and Gopalkrishnan, V. and Žliobaitė, I. and Pechenizkiy, M. and Hoi, S.C.H.},
  title = {Predictive Handling of Asynchronous Concept Drifts in Distributed Environments},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2013},
  volume = {25},
  number = {10},
  pages = {2343-2355},
  doi = {http://doi.org/10.1109/TKDE.2012.172},
  file = {Ang2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Ang2013.pdf:PDF}
}
technical report Žliobaitė, I. & Pechenizkiy, M. (2013) Predictive User Modeling with Actionable Attributes CoRR, arXiv: 1312.6558.

BibTeX: 
@techreport{DBLP:journals/corr/ZliobaiteP13,
  author = {Indrė Žliobaitė and Mykola Pechenizkiy},
  title = {Predictive User Modeling with Actionable Attributes},
  school = {CoRR},
  year = {2013},
  number = {arXiv: 1312.6558}
}
conference proceedings Rodrigues, P.P., Pechenizkiy, M., Gama, J., Cruz-Correia, R., Liu, J., Traina, A.J.M., Lucas, P.J.F. & Soda, P. (2013) "Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, June 20-22, 2013", In CBMS, IEEE.

BibTeX: 
@proceedings{DBLP:conf/cbms/2013,
  author = {Pedro Pereira Rodrigues and Mykola Pechenizkiy and João Gama and Ricardo Cruz-Correia and Jiming Liu and Agma J. M. Traina and Peter J. F. Lucas and Paolo Soda},
  title = {Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, June 20-22, 2013},
  booktitle = {CBMS},
  publisher = {IEEE},
  year = {2013},
  doi = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6607262},
  pdf = {stamp.jsp?tp=&arnumber=6627746:http//ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6627746:PDF}  
}
workshop Tromp, E. & Pechenizkiy, M. (2013) RBEM: A Rule Based Approach to Polarity Detection, In Proceedings of the Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM@KDD2013), ACM.

BibTeX: 
@inproceedings{TrompWISDOM2013,
  author = {Erik Tromp and Mykola Pechenizkiy},
  title = {RBEM: A Rule Based Approach to Polarity Detection},
  booktitle = {Proceedings of the Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM@KDD2013)},
  publisher = {ACM},
  year = {2013},
  file = {RBEM_WISDOM2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/RBEM_WISDOM2013.pdf:PDF}
}
conference Kurniawan, H., Maslov, A. & Pechenizkiy, M. (2013) Stress Detection from Speech and Galvanic Skin Response Signals, In Proceedings of 26th IEEE Conference on Computer-Based Medical Systems (CBMS 2013), IEEE Computer Society, pp. 209-214.

BibTeX: 
@inproceedings{KurniawanCBMS2013,
  author = {Hindra Kurniawan and Alexandr Maslov and Mykola Pechenizkiy},
  title = {Stress Detection from Speech and Galvanic Skin Response Signals},
  booktitle = {Proceedings of 26th IEEE Conference on Computer-Based Medical Systems (CBMS 2013)},
  publisher = {IEEE Computer Society},
  year = {2013},
  pages = {209-214},
  doi = {http://doi.org/10.1109/CBMS.2013.6627790},
  file = {KurniawanCBMS2013.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KurniawanCBMS2013.pdf:PDF}
}
book chapter Kamiran, F., Calders, T. & Pechenizkiy, M. (2013) "Techniques for Discrimination-Free Predictive Models", In Discrimination and Privacy in the Information Society, Vol. 3, Springer Berlin Heidelberg, pp. 223-239.

BibTeX: 
@incollection{KamiranBook2013,
  author = {Kamiran, Faisal and Calders, Toon and Pechenizkiy, Mykola},
  title = {Techniques for Discrimination-Free Predictive Models},
  booktitle = {Discrimination and Privacy in the Information Society},
  publisher = {Springer Berlin Heidelberg},
  year = {2013},
  volume = {3},
  pages = {223-239},
  doi = {http://doi.org/10.1007/978-3-642-30487-3_12}  
}
tutorial abstract Bifet, A., Gama, J., Gavaldà, R., Krempl, G., Pechenizkiy, M., Pfahringer, B., Spiliopoulou, M. & Žliobaitė, I. (Eds.) (2012) Advanced Topics on Data Stream Mining.

BibTeX: 
@misc{Bifet2012tutorial,,
  title = {Advanced Topics on Data Stream Mining},
  booktitle = {Tutorial at the 23rd Europ. Conf. on Machine Learning and 16th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'12)},
  year = {2012},
  url = {https://sites.google.com/site/advancedstreamingtutorial/},
  doi = {https://sites.google.com/site/advancedstreamingtutorial/}
}
workshop Calders, T. & Pechenizkiy, M. (2012) Cost-sensitive classification problem, In Workshop on Teaching Machine Learning @ ICML 2012, pp. 1-6.

BibTeX: 
@inproceedings{Calders_TML12,
  author = {Toon Calders and Mykola Pechenizkiy},
  title = {Cost-sensitive classification problem},
  booktitle = {Workshop on Teaching Machine Learning @ ICML 2012},
  year = {2012},
  pages = {1--6},
  url = {http://repository.tue.nl/750220},
  file = {Calders-TML.pdf:http//swarmlab.unimaas.nl/TeachingML/papers/Calders-TML.pdf:PDF}
}
poster Pechenizkiy, M., Trcka, N., De Bra, P. & Toledo, P. (2012) CurriM: Curriculum Mining, In Proc. of 5th International Conference on Educational Data Mining, pp. 216-217.

BibTeX: 
@inproceedings{PechenizkiyEDM2012,
  author = {Mykola Pechenizkiy and Nikola Trcka and De Bra, Paul and Pedro Toledo},
  title = {CurriM: Curriculum Mining},
  booktitle = {Proc. of 5th International Conference on Educational Data Mining},
  editor = {Kalina Yacef and Osmar Zaïane and Arnon Hershkovitz and Michael Yudelson and John Stamper},
  year = {2012},
  pages = {216-217},
  url = {http://educationaldatamining.org/EDM2012/index.php?page=proceedings},
  file = {edm2012_poster_11.pdf:http//educationaldatamining.org/EDM2012/uploads/procs/Posters/edm2012_poster_11.pdf:PDF}
}
conference Chambers, L., Tromp, E., Pechenizkiy, M. & Gaber, M. (2012) Mobile sentiment analysis, In Proc. of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Frontiers in Artificial Intelligence and Applications, 243, IOS Press, pp. 470-479.

BibTeX: 
@inproceedings{ChambersKBIES2012,
  author = {Chambers, L. and Tromp, E. and Pechenizkiy, M. and Gaber, M.},
  title = {Mobile sentiment analysis},
  booktitle = {Proc. of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems},
  editor = {Manuel Graña and Carlos Toro and Jorge Posada and Robert J. Howlett and Lakhmi C. Jain},
  publisher = {IOS Press},
  year = {2012},
  volume = {243},
  pages = {470-479},
  doi = {http://doi.org/10.3233/978-1-61499-105-2-470}
}
conference Apeh, E., Žliobaitė, I., Pechenizkiy, M. & Gabrys, B. (2012) Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales, In Proc. of the 32nd Annual Int. Conf. of the British Computer Society's Specialist Group on Arti.cial Intelligence (SGAI'12), pp. 213-218.

BibTeX: 
@inproceedings{Apeh2012,
  author = {Apeh, E. and Žliobaitė, I. and Pechenizkiy, M. and Gabrys, B.},
  title = {Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales},
  booktitle = {Proc. of the 32nd Annual Int. Conf. of the British Computer Society's Specialist Group on Arti.cial Intelligence (SGAI'12)},
  year = {2012},
  pages = {213-218},
  doi = {http://doi.org/10.1007/978-1-4471-4739-8_17},
  file = {FullMulticlassClassification.pdf?attredirects=0:https//sites.google.com/site/zliobaitefiles/FullMulticlassClassification.pdf?attredirects=0:PDF}
}
proceedings Soda, P., Tortorella, F., Antani, S., Pechenizkiy, M., Cannataro, M. & Tsymbal, A. (2012) "Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012)", IEEE Computer Society.

BibTeX: 
@proceedings{CBMS2012,
  author = {Soda, P. and Tortorella, F. and Antani, S. and Pechenizkiy, M. and Cannataro, M. and Tsymbal, A.},
  title = {Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012)},
  publisher = {IEEE Computer Society},
  year = {2012},
  doi = {http://doi.org/10.1109/CBMS.2012.6266288}  
}
workshop Maslov, A., Pechenizkiy, M., Kärkkäinen, T. & Tähtinen, M. (2012) Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings, In Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data, SensorKDD '12, ACM, pp. 25-33.

BibTeX: 
@inproceedings{Maslov2012,
  author = {Maslov, Alexandr and Pechenizkiy, Mykola and Kärkkäinen, Tommi and Tähtinen, Matti},
  title = {Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings},
  booktitle = {Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data},
  publisher = {ACM},
  year = {2012},
  pages = {25--33},
  url = {http://doi.acm.org/10.1145/2350182.2350185},
  doi = {http://doi.org/10.1145/2350182.2350185},
  file = {MaslovSensorsKDD2012.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MaslovSensorsKDD2012.pdf:PDF}
}
journal Zafra, A., Pechenizkiy, M. & Ventura, S. (2012) ReliefF-MI: An extension of ReliefF to multiple instance learning, Neurocomputing, 75(1), pp. 210-218.

BibTeX: 
@article{ZafraNEUCOM2011,
  author = {Amelia Zafra and Mykola Pechenizkiy and Sebastián Ventura},
  title = {ReliefF-MI: An extension of ReliefF to multiple instance learning},
  journal = {Neurocomputing},
  year = {2012},
  volume = {75},
  number = {1},
  pages = {210--218},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231211003997},
  doi = {http://doi.org/10.1016/j.neucom.2011.03.052},
  file = {ZafraNEUCOM2011.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZafraNEUCOM2011.pdf:PDF}
}
conference, short Bakker, J., Holenderski, L., Kocielnik, R., Pechenizkiy, M. & Sidorova, N. (2012) Stess@work: From Measuring Stress to its Understanding, Prediction and Handling with Personalized Coaching, In Proceedings of ACM SIGHIT International Health Informatics Symposium (IHI 2012), ACM Press, pp. 673-678.

BibTeX: 
@inproceedings{BakerIHI2012,
  author = {Jorn Bakker and Leszek Holenderski and Rafal Kocielnik and Mykola Pechenizkiy and Natalia Sidorova},
  title = {Stess@work: From Measuring Stress to its Understanding, Prediction and Handling with Personalized Coaching},
  booktitle = {Proceedings of ACM SIGHIT International Health Informatics Symposium (IHI 2012)},
  publisher = {ACM Press},
  year = {2012},
  pages = {673-678},
  doi = {http://doi.org/10.1145/2110363.2110439},
  file = {BakkerIHI2012.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/BakkerIHI2012.pdf:PDF}
}
poster Kocielnik, R., Pechenizkiy, M. & Sidorova, N. (2012) Stress Analytics in Education, In Proc. of 5th International Conference on Educational Data Mining, pp. 236-237.

BibTeX: 
@inproceedings{KocielnikEDM2012,
  author = {Rafal Kocielnik and Mykola Pechenizkiy and Natalia Sidorova},
  title = {Stress Analytics in Education},
  booktitle = {Proc. of 5th International Conference on Educational Data Mining},
  editor = {Kalina Yacef and Osmar Zaïane and Arnon Hershkovitz and Michael Yudelson and John Stamper},
  year = {2012},
  pages = {236-237},
  url = {http://educationaldatamining.org/EDM2012/index.php?page=proceedings},
  file = {edm2012_poster_7.pdf:http//educationaldatamining.org/EDM2012/uploads/procs/Posters/edm2012_poster_7.pdf:PDF}
}
book chapter Calders, T., Fletcher, G.H.L., Kamiran, F. & Pechenizkiy, M. (2012) "Technologies for Dealing with Information Overload An Engineers’ Point of View", In Information Overload: An International Challenge for Professional Engineers and Technical Communicators, John Wiley & Sons Inc, pp. 175-202.

BibTeX: 
@incollection{CaldersBook2011,
  author = {Toon Calders and Fletcher, George H.L. and Faisal Kamiran and Mykola Pechenizkiy},
  title = {Technologies for Dealing with Information Overload An Engineers’ Point of View},
  booktitle = {Information Overload: An International Challenge for Professional Engineers and Technical Communicators},
  publisher = {John Wiley & Sons Inc},
  year = {2012},
  pages = {175--202},
  doi = {http://doi.org/10.1002/9781118360491.ch9}  
}
journal Soda, P., Antani, S., Tortorella, F., Cannataro, M., Pechenizkiy, M. & Tsymbal, A. (2012) Trends in computer-based medical systems, SIGHIT Record, 2(2), pp. 46-50.

BibTeX: 
@article{SodaATCPT12,
  author = {Paolo Soda and Sameer Antani and Francesco Tortorella and Mario Cannataro and Mykola Pechenizkiy and Alexey Tsymbal},
  title = {Trends in computer-based medical systems},
  journal = {SIGHIT Record},
  year = {2012},
  volume = {2},
  number = {2},
  pages = {46-50}
}
workshop Knutov, E., De Bra, P. & Pechenizkiy, M. (2011) Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework, In Proceedings of 2nd DAH'2011 Workshop on Dynamic and Adaptive Hypertext, pp. to appear.

BibTeX: 
@inproceedings{KnutovDAH2011,
  author = {E. Knutov and De Bra, P. and M. Pechenizkiy},
  title = {Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework},
  booktitle = {Proceedings of 2nd DAH'2011 Workshop on Dynamic and Adaptive Hypertext},
  year = {2011},
  pages = {to appear},
  file = {dah11_paper_5.pdf:http//www.win.tue.nl/ eknutov/dah11/papers/dah11_paper_5.pdf:PDF}
}
poster Knutov, E., De Bra, P. & Pechenizkiy, M. (2011) Adaptive Hypermedia Systems Extensive Analysis Approach, In Proceedings of 22nd ACM Conference on Hypertext and Hypermedia (HT '2011), ACM Press.

BibTeX: 
@inproceedings{KnutovHT2011,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Adaptive Hypermedia Systems Extensive Analysis Approach},
  booktitle = {Proceedings of 22nd ACM Conference on Hypertext and Hypermedia (HT '2011)},
  publisher = {ACM Press},
  year = {2011},
  file = {ht2011_ah.analysis.abstract.pdf:http//www.win.tue.nl/ eknutov/papers/ht2011_ah.analysis.abstract.pdf:PDF}
}
journal Žliobaitė, I., Bakker, J. & Pechenizkiy, M. (2011) Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?, Expert Systems With Applications, 39(1), pp. 806-815.

BibTeX: 
@article{ZliobaiteESA2011,
  author = {Indre Žliobaitė and Jorn Bakker and Mykola Pechenizkiy},
  title = {Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?},
  journal = {Expert Systems With Applications},
  year = {2011},
  volume = {39},
  number = {1},
  pages = {806--815},
  doi = {http://doi.org/10.1016/j.eswa.2011.07.078},
  file = {sligro_ESA.pdf?attredirects=0:https//sites.google.com/site/zliobaite/sligro_ESA.pdf?attredirects=0:PDF}
}
conference Knutov, E., De Bra, P., Smits, D. & Pechenizkiy, M. (2011) Bridging Navigation, Search and Adaptation - Adaptive Hypermedia Models Evolution, In Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST'2011), SciTePress, pp. 314-321.

BibTeX: 
@inproceedings{KnutovWEBIST2011,
  author = {Evgeny Knutov and De Bra, Paul and David Smits and Mykola Pechenizkiy},
  title = {Bridging Navigation, Search and Adaptation - Adaptive Hypermedia Models Evolution},
  booktitle = {Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST'2011)},
  editor = {José Cordeiro and Joaquim Filipe},
  publisher = {SciTePress},
  year = {2011},
  pages = {314-321},
  file = {webist2011_AHS.evolution.pdf:http//www.win.tue.nl/ eknutov/papers/webist2011_AHS.evolution.pdf:PDF}
}
workshop Hannon, J., Knutov, E., De Bra, P., Pechenizkiy, M., Smyth, B. & McCarthy, K. (2011) Bridging Recommendation and Adaptation: Generic Adaptation Framework - Twittomender compliance study, In Proceedings of 2nd DAH'2011 Workshop on Dynamic and Adaptive Hypertext, pp. to appear.

BibTeX: 
@inproceedings{HannonDAH2011,
  author = {J. Hannon and E. Knutov and De Bra, P. and M. Pechenizkiy and B. Smyth and K. McCarthy},
  title = {Bridging Recommendation and Adaptation: Generic Adaptation Framework - Twittomender compliance study},
  booktitle = {Proceedings of 2nd DAH'2011 Workshop on Dynamic and Adaptive Hypertext},
  year = {2011},
  pages = {to appear},
  file = {dah11_paper_1.pdf:http//www.win.tue.nl/ eknutov/dah11/papers/dah11_paper_1.pdf:PDF}
}
conference Mazhelis, O., Žliobaitė, I. & Pechenizkiy, M. (2011) Context-aware Personal Route Recognition, In Proc. of the 14th International Conf. on Discovery Science (DS 2011), Springer LNCS(6926), pp. 221-235.

BibTeX: 
@inproceedings{MazhelisDS2011,
  author = {Mazhelis, O. and Žliobaitė, I. and Pechenizkiy, M.},
  title = {Context-aware Personal Route Recognition},
  booktitle = {Proc. of the 14th International Conf. on Discovery Science (DS 2011)},
  year = {2011},
  number = {6926},
  pages = {221--235},
  file = {context_route_camera.pdf?attredirects=0:https//sites.google.com/site/zliobaite/context_route_camera.pdf?attredirects=0:PDF}
}
journal Knutov, E., De Bra, P. & Pechenizkiy, M. (2011) Generic Adaptation Framework: a Process-Oriented Perspective, Journal of Digital Information, 12(1).

BibTeX: 
@article{KnutoveJODI2011,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Generic Adaptation Framework: a Process-Oriented Perspective},
  journal = {Journal of Digital Information},
  year = {2011},
  volume = {12},
  number = {1},
  file = {1739:http//journals.tdl.org/jodi/article/view/965/1739:PDF}
}
conference Tromp, E. & Pechenizkiy, M. (2011) Graph-Based N-gram Language Identification on Short Texts, In Proceedings of the Twentieth Belgian Dutch Conference on Machine Learning (Benelearn 2011), pp. 27-34.

BibTeX: 
@inproceedings{TrompBENELEARN2011,
  author = {Erik Tromp and Mykola Pechenizkiy},
  title = {Graph-Based N-gram Language Identification on Short Texts},
  booktitle = {Proceedings of the Twentieth Belgian Dutch Conference on Machine Learning (Benelearn 2011)},
  year = {2011},
  pages = {27-34},
  file = {TrompPechenizkiy_LIGA_Benelearn11.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TrompPechenizkiy_LIGA_Benelearn11.pdf:PDF}
}
conference Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I. & Pechenizkiy, M. (2011) Handling Concept Drift in Process Mining, In Proceedings of 23rd International Conference on Advanced Information Systems Engineering CAiSE'2011, Lecture Notes in Computer Science, 6741, Springer, pp. 391-405.

BibTeX: 
@inproceedings{BoseCAISE2011,
  author = {R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst and Indrė Žliobaitė and Mykola Pechenizkiy},
  title = {Handling Concept Drift in Process Mining},
  booktitle = {Proceedings of 23rd International Conference on Advanced Information Systems Engineering CAiSE'2011},
  editor = {Haralambos Mouratidis and Colette Rolland},
  publisher = {Springer},
  year = {2011},
  volume = {6741},
  pages = {391-405},
  doi = {http://doi.org/10.1007/978-3-642-21640-4_30},
  file = {jc-conceptdrift-caise-2011.pdf:http//www.win.tue.nl/ jcbose/jc-conceptdrift-caise-2011.pdf:PDF}
}
journal Calders, T. & Pechenizkiy, M. (2011) Introduction to the special section on educational data mining, SIGKDD Explorations, 13(2), pp. 3-6.

BibTeX: 
@article{CaldersPechenizkiy2011,
  author = {Toon Calders and Mykola Pechenizkiy},
  title = {Introduction to the special section on educational data mining},
  journal = {SIGKDD Explorations},
  year = {2011},
  volume = {13},
  number = {2},
  pages = {3-6},
  doi = {http://doi.org/10.1145/2207243.2207245},
  file = {CaldersIntroEDM11.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/CaldersIntroEDM11.pdf:PDF}
}
workshop Rodrigues, P.P., Pechenizkiy, M., Gaber, M.M. & Gama, J. (2011) Learning from medical data streams: an introduction, In Proceedings of the Learning from Medical Data Streams (LEMEDS'11) workshop @ AIME'2011 Conference, pp. 1-6.

BibTeX: 
@inproceedings{RodriguesLMDS2011,
  author = {Rodrigues, Pedro Pereira and Mykola Pechenizkiy and Gaber, Mohamed Medhat and Joao Gama},
  title = {Learning from medical data streams: an introduction},
  booktitle = {Proceedings of the Learning from Medical Data Streams (LEMEDS'11) workshop @ AIME'2011 Conference},
  year = {2011},
  pages = {1-6},
  file = {lemeds11_submission_6.pdf:http//www.liaad.up.pt/ pprodrigues/lmds11/lemeds2011/lemeds11_submission_6.pdf:PDF}
}
editorial Khan, L., Pechenizkiy, M. & Žliobaitė, I. (2011) Preface to the Handling Concept Drift and Reoccurring Contexts in Adaptive Information Systems Workshop, In Proceedings of the 11th IEEE International Conference on Data Mining Workshops, pp. xxxvi-xxxvii.

BibTeX: 
@inproceedings{Khan2011,
  author = {Lim Khan and Mykola Pechenizkiy and Indrė Žliobaitė},
  title = {Preface to the Handling Concept Drift and Reoccurring Contexts in Adaptive Information Systems Workshop},
  booktitle = {Proceedings of the 11th IEEE International Conference on Data Mining Workshops},
  year = {2011},
  pages = {xxxvi-xxxvii},
  doi = {http://doi.org/10.1109/ICDMW.2011.195}
}
proceedings Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C. & Stamper, J. (Eds.) (2011) Proceedings of the 4th International Conference on Educational Data Mining.

BibTeX: 
@misc{PechenizkiyEDM2011,,
  title = {Proceedings of the 4th International Conference on Educational Data Mining},
  publisher = {TU/e printservice},
  year = {2011},
  url = {http://educationaldatamining.org/EDM2011/proceedings-2},
  file = {edm11_proceedings.pdf:http//educationaldatamining.org/EDM2011/wp-content/uploads/proc/edm11_proceedings.pdf:PDF}
}
proceedings Rodrigues, P.P., Pechenizkiy, M., Gaber, M.M. & Gama, J. (Eds.) (2011) Proceedings of the Learning from Medical Data Streams (LEMEDS'11) workshop @ AIME'2011 Conference.

BibTeX: 
@misc{RodriguesLEMEDS2011,,
  title = {Proceedings of the Learning from Medical Data Streams (LEMEDS'11) workshop @ AIME'2011 Conference},
  year = {2011},
  url = {http://www.liaad.up.pt/ pprodrigues/lmds11/},
  file = {lemeds11papers.pdf:http//www.liaad.up.pt/ pprodrigues/lmds11/lemeds2011/lemeds11papers.pdf:PDF}
}
demo paper Tromp, E. & Pechenizkiy, M. (2011) SentiCorr: Multilingual Sentiment Analysis of Personal Correspondence, In Proceedings of IEEE ICDM 2011 Workshops, IEEE, pp. 470-479.

BibTeX: 
@inproceedings{TrompICDM2011,
  author = {Erik Tromp and Mykola Pechenizkiy},
  title = {SentiCorr: Multilingual Sentiment Analysis of Personal Correspondence},
  booktitle = {Proceedings of IEEE ICDM 2011 Workshops},
  editor = {Myra Spiliopoulou and Haixun Wang and Diane J. Cook and Jian Pei and Wei Wang and Osmar R. Za\iane and Xindong Wu},
  publisher = {IEEE},
  year = {2011},
  pages = {470-479},
  doi = {http://doi.org/10.1109/ICDMW.2011.152},
  file = {TrompICDM2011.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TrompICDM2011.pdf:PDF}
}
special issue Calders, T. & Pechenizkiy, M. (2011) Special section on educational data mining, SIGKDD Explorations, 13(2).

BibTeX: 
@article{Calders2011,
  author = {Toon Calders and Mykola Pechenizkiy},
  title = {Special section on educational data mining},
  journal = {SIGKDD Explorations},
  year = {2011},
  volume = {13},
  number = {2},
  doi = {http://doi.org/10.1145/2207243.2207245}
}
workshop Bakker, J., Pechenizkiy, M. & Sidorova, N. (2011) What's your current stress level? Detection of stress patterns from GSR sensor data, In Proceedings of ICDM Workshops. 2nd HACDAIS Workshop @ ICDM 2011 (HACDAIS 2011), pp. 573-580.

BibTeX: 
@inproceedings{BakerHACDAIS2011,
  author = {Jorn Bakker and Mykola Pechenizkiy and Natalia Sidorova},
  title = {What's your current stress level? Detection of stress patterns from GSR sensor data},
  booktitle = {Proceedings of ICDM Workshops. 2nd HACDAIS Workshop @ ICDM 2011 (HACDAIS 2011)},
  year = {2011},
  pages = {573-580},
  doi = {http://doi.org/10.1109/ICDMW.2011.178},
  file = {Bakker_HaCDAIS2011.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/Bakker_HaCDAIS2011.pdf:PDF}
}
conference Puuronen, S., Pechenizkiy, M., Vasilyeva, E. & Tesanovic, A. (2010) A Holistic Framework for Understanding Acceptance of Remote Patient Management (RPM) Systems by Non-Professional Users, In Proceedings of IEEE Conference on Computer-Based Medical Systems, pp. 426 - 431.

BibTeX: 
@inproceedings{PuuronenCBMS2010,
  author = {Seppo Puuronen and Mykola Pechenizkiy and Ekaterina Vasilyeva and Aleksandra Tesanovic},
  title = {A Holistic Framework for Understanding Acceptance of Remote Patient Management (RPM) Systems by Non-Professional Users},
  booktitle = {Proceedings of IEEE Conference on Computer-Based Medical Systems},
  year = {2010},
  pages = {426 - 431},
  doi = {http://doi.org/10.1109/CBMS.2010.6042682},
  file = {PuuronenCBMS2010.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PuuronenCBMS2010.pdf:PDF}
}
conference, short Knutov, E., De Bra, P. & Pechenizkiy, M. (2010) Adaptation and search: from Dexter and AHAM to GAF, In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT'10), ACM Press, pp. 281-282.

BibTeX: 
@inproceedings{KnutovHT2010b,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Adaptation and search: from Dexter and AHAM to GAF},
  booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT'10)},
  editor = {Mark H. Chignell and Elaine Toms},
  publisher = {ACM Press},
  year = {2010},
  pages = {281-282},
  doi = {http://doi.org/10.1145/1810617.1810675},
  file = {KnutovHT2010b.pdf:KnutovHT2010b.pdf:PDF}
}
poster Knutov, E., De Bra, P. & Pechenizkiy, M. (2010) Bridging Versioning and Adaptive Hypermedia in the Dynamic Web, In Adjunct Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization: Posters and Demonstratione (UMAP 2010), pp. 13-15.

BibTeX: 
@inproceedings{KnutovUMAP10,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Bridging Versioning and Adaptive Hypermedia in the Dynamic Web},
  booktitle = {Adjunct Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization: Posters and Demonstratione (UMAP 2010)},
  editor = {Fabian Bohnert and Luz M. Quiroga},
  year = {2010},
  pages = {13-15},
  file = {KnutovUMAP10.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KnutovUMAP10.pdf:PDF}
}
conference, short Romero, C., Ventura, S., Vasilyeva, E. & Pechenizkiy, M. (2010) Class Association Rules Mining from Students' Test Data, In Educational Data Mining 2010, The 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA, June 11-13, 2010. Proceedings, www.educationaldatamining.org, pp. 317-318.

BibTeX: 
@inproceedings{RomeroEDM2010,
  author = {Cristóbal Romero and Sebastián Ventura and Ekaterina Vasilyeva and Mykola Pechenizkiy},
  title = {Class Association Rules Mining from Students' Test Data},
  booktitle = {Educational Data Mining 2010, The 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA, June 11-13, 2010. Proceedings},
  editor = {Ryan Shaun Joazeiro de Baker and Agathe Merceron and Philip I. Pavlik Jr.},
  publisher = {www.educationaldatamining.org},
  year = {2010},
  pages = {317-318},
  file = {RomeroEDM2010.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/RomeroEDM2010.pdf:PDF}
}
conference, short Kamiran, F., Calders, T. & Pechenizkiy, M. (2010) Discrimination Aware Decision Tree Learning, In Proceeding of ICDM 2010, IEEE Computer Society, pp. 869-874.

BibTeX: 
@inproceedings{KamiranICDM2010,
  author = {Faisal Kamiran and Toon Calders and Mykola Pechenizkiy},
  title = {Discrimination Aware Decision Tree Learning},
  booktitle = {Proceeding of ICDM 2010},
  publisher = {IEEE Computer Society},
  year = {2010},
  pages = {869-874},
  file = {KamiranICDM2010.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KamiranICDM2010.pdf:PDF}
}
technical report Kamiran, F., Calders, T. & Pechenizkiy, M. (2010) Discrimination Aware Decision Tree Learning Eindhoven University of Technology, Dept. Math. and Computer Science, CS-Report 10-13.

BibTeX: 
@techreport{KamiranTechReport2010,
  author = {Faisal Kamiran and Toon Calders and Mykola Pechenizkiy},
  title = {Discrimination Aware Decision Tree Learning},
  school = {Eindhoven University of Technology, Dept. Math. and Computer Science},
  year = {2010},
  number = {CS-Report 10-13},
  file = {TR10-13.pdf:http//wwwis.win.tue.nl/ tcalders/pubs/TR10-13.pdf:PDF}
}
conference Zafra, A., Pechenizkiy, M. & Ventura, S. (2010) Feature Selection is the ReliefF for Multiple Instance Learning, In Proceedings of ISDA 2010 Conference, IEEE Computer Society, pp. 525-532.

BibTeX: 
@inproceedings{ZafraISDA2010,
  author = {Amelia Zafra and Mykola Pechenizkiy and Sebastián Ventura},
  title = {Feature Selection is the ReliefF for Multiple Instance Learning},
  booktitle = {Proceedings of ISDA 2010 Conference},
  publisher = {IEEE Computer Society},
  year = {2010},
  pages = {525--532},
  doi = {http://doi.org/10.1109/ISDA.2010.5687210},
  file = {ZafraISDA2010.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZafraISDA2010.pdf:PDF}
}
workshop Knutov, E., De Bra, P. & Pechenizkiy, M. (2010) Generic Adaptation Process, In Proceedings of Workshop on Architectures and Building Blocks of Web-based User-Adaptive Systems (co-located with UMAP 2010 conference), pp. 13-24.

BibTeX: 
@inproceedings{KnutovWABBWUAS2010,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Generic Adaptation Process},
  booktitle = {Proceedings of Workshop on Architectures and Building Blocks of Web-based User-Adaptive Systems (co-located with UMAP 2010 conference)},
  year = {2010},
  pages = {13--24},
  file = {paper2.pdf:http//CEUR-WS.org/Vol-609/paper2.pdf:PDF}
}
edited volume Romero, C., Ventura, S., Pechenizkiy, M. & Baker, R. (Eds.) (2010) Handbook of Educational Data Mining, Chapman&Hall/CRC Data Mining and Knowledge Discovery Series, CRC Press, Taylor\&Francis Group.

BibTeX: 
@book{RomeroEDMBook10,,
  title = {Handbook of Educational Data Mining},
  publisher = {CRC Press, Taylor&Francis Group},
  year = {2010},
  url = {http://www.crcpress.com/product/isbn/9781439804575},
  doi = {http://www.crcpress.com/product/isbn/9781439804575}
}
tutorial abstract Pechenizkiy, M. & Žliobaitė, I. (2010) Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions, In Proceedings of IEEE Conference on Computer-Based Medical Systems, pp. 5.

BibTeX: 
@inproceedings{PechenizkiyZliobaiteCBMS2010,
  author = {Mykola Pechenizkiy and Indrė Žliobaitė},
  title = {Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions},
  booktitle = {Proceedings of IEEE Conference on Computer-Based Medical Systems},
  year = {2010},
  pages = {5},
  doi = {http://doi.org/10.1109/CBMS.2010.6042653}
}
conference Pechenizkiy, M., Vasilyeva, E., Žliobaitė, I., Tesanovic, A. & Manev, G. (2010) Heart failure hospitalization prediction in remote patient management systems, In IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia, October 12-15, 2010, IEEE Computer Society, pp. 44-49.

BibTeX: 
@inproceedings{PechenizkiyCBMS2010,
  author = {Mykola Pechenizkiy and Ekaterina Vasilyeva and Indrė Žliobaitė and Aleksandra Tesanovic and Goran Manev},
  title = {Heart failure hospitalization prediction in remote patient management systems},
  booktitle = {IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia, October 12-15, 2010},
  editor = {Tharam S. Dillon and Daniel L. Rubin and William M. Gallagher and Amandeep S. Sidhu and Alexey Tsymbal},
  publisher = {IEEE Computer Society},
  year = {2010},
  pages = {44--49},
  doi = {http://doi.org/10.1109/CBMS.2010.6042612},
  file = {https://doi.org/10.1109/CBMS.2010.6042612}
}
book chapter Romero, C., Ventura, S., Pechenizkiy, M. & Baker, R. (2010) "Introduction Chapter", In Handbook of Educational Data Mining, London: CRC Press.

BibTeX: 
@incollection{Romero2010,
  author = {Cristobal Romero and Sebastian Ventura and Mykola Pechenizkiy and Ryan Baker},
  title = {Introduction Chapter},
  booktitle = {Handbook of Educational Data Mining},
  publisher = {London: CRC Press},
  year = {2010},
  url = {http://www.crcpress.com/product/isbn/9781439804575;jsessionid=E91eC6ilcL9RXOWLV4osrQ**}  
}
editorial Soda, P., Pechenizkiy, M., Tortorella, F. & Tsymbal, A. (2010) Knowledge discovery and computer-based decision support in biomedicine, Artificial Intelligence in Medicine, 50(1), pp. 1-2.

BibTeX: 
@article{Sodaetal2010,
  author = {Paolo Soda and Mykola Pechenizkiy and Francesco Tortorella and Alexey Tsymbal},
  title = {Knowledge discovery and computer-based decision support in biomedicine},
  journal = {Artificial Intelligence in Medicine},
  year = {2010},
  volume = {50},
  number = {1},
  pages = {1-2},
  doi = {http://doi.org/10.1016/j.artmed.2010.06.001}
}
workshop Žliobaitė, I. & Pechenizkiy, M. (2010) "Learning with Actionable Attributes: attention - boundary cases!", In Proceedings of Domain Driven Data Mining Workshop (DDDM2010) @ ICDM 2010, IEEE Computer Society, pp. 1021-1028.

BibTeX: 
@conference{ZliobaiteICDM2010,
  author = {Indrė Žliobaitė and Mykola Pechenizkiy},
  title = {Learning with Actionable Attributes: attention - boundary cases!},
  booktitle = {Proceedings of Domain Driven Data Mining Workshop (DDDM2010) @ ICDM 2010},
  publisher = {IEEE Computer Society},
  year = {2010},
  pages = {1021--1028},
  pdf = {ZliobaiteICDM2010.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZliobaiteICDM2010.pdf:PDF}  
}
poster Pechenizkiy, M., Tesanovic, A., Manev, G., Vasilyeva, E., Knutov, E., Verwer, S. & De Bra, P. (2010) Patient Condition Modeling in Remote Patient Management: Hospitalization Prediction, In Adjunct Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization: Posters and Demonstration (UMAP 2010), pp. 34-36.

BibTeX: 
@inproceedings{PechenizkiyUMAP10,
  author = {M. Pechenizkiy and A. Tesanovic and G. Manev and E. Vasilyeva and E. Knutov and S. Verwer and De Bra, P.},
  title = {Patient Condition Modeling in Remote Patient Management: Hospitalization Prediction},
  booktitle = {Adjunct Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization: Posters and Demonstration (UMAP 2010)},
  editor = {Fabian Bohnert and Luz M. Quiroga},
  year = {2010},
  pages = {34-36},
  file = {PechenizkiyUMAP10.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyUMAP10.pdf:PDF}
}
editorial Pechenizkiy, M. & Žliobaitė, I. (2010) "Preface to the Proceedings of the Workshop on "Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions" (HaCDAIS'2010), held in conjunction with ECML/PKDD 2010", pp. iii-iv.

BibTeX: 
@other{PechenizkiyEditorialHaCDAIS2010,
  author = {Mykola Pechenizkiy and Indrė Žliobaitė},
  title = {Preface to the Proceedings of the Workshop on "Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions" (HaCDAIS'2010), held in conjunction with ECML/PKDD 2010},
  year = {2010},
  pages = {iii-iv},
  pdf = {coverpages.pdf:http//wwwis.win.tue.nl/hacdais2010/coverpages.pdf:PDF}  
}
proceedings Abel, F., Herder, E., Houben, G.-J., Pechenizkiy, M. & Yudelson, M. (Eds.) (2010) Proceedings of the International Workshop on Architectures and Building Blocks of Web-Based User-Adaptive Systems (WABBWUAS 2010).

BibTeX: 
@misc{CEUR-WS.org/Vol-609,,
  title = {Proceedings of the International Workshop on Architectures and Building Blocks of Web-Based User-Adaptive Systems (WABBWUAS 2010)},
  publisher = {CEUR-WS.org},
  year = {2010},
  volume = {609},
  url = {http://ceur-ws.org/Vol-609},
  file = {wabbwuas-proceedings.pdf:http//ceur-ws.org/Vol-609/wabbwuas-proceedings.pdf:PDF}
}
edited volume Pechenizkiy, M. & Žliobaitė, I. (Eds.) (2010) Proceedings of the Workshop on "Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions" (HaCDAIS'2010) @ ECML/PKDD 2010.

BibTeX: 
@book{PechenizkiyZliobaite_HaCDAIS2010,,
  title = {Proceedings of the Workshop on "Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions" (HaCDAIS'2010) @ ECML/PKDD 2010},
  year = {2010},
  file = {hacdais2010_proceedings.pdf:http//wwwis.win.tue.nl/hacdais2010/hacdais2010_proceedings.pdf:PDF}
}
book chapter Trčka, N., Pechenizkiy, M. & van der Aalst, W. (2010) "Process Mining from Educational Data (Chapter 9)", In Handbook of Educational Data Mining, London: CRC Press, pp. 123-142.

BibTeX: 
@incollection{TrckaEDMBook10,
  author = {Nikola Trčka and Mykola Pechenizkiy and van der Aalst, Wil},
  title = {Process Mining from Educational Data (Chapter 9)},
  booktitle = {Handbook of Educational Data Mining},
  publisher = {London: CRC Press},
  year = {2010},
  pages = {123--142},
  url = {http://www.crcpress.com/product/isbn/9781439804575;jsessionid=E91eC6ilcL9RXOWLV4osrQ**}  
}
conference Knutov, E., De Bra, P. & Pechenizkiy, M. (2010) Provenance meets Adaptive Hypermedia, In Proceedings of ACM Hypertext Conference (HT'10), ACM Press, pp. 93-98.

BibTeX: 
@inproceedings{KnutovHT10a,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Provenance meets Adaptive Hypermedia},
  booktitle = {Proceedings of ACM Hypertext Conference (HT'10)},
  publisher = {ACM Press},
  year = {2010},
  pages = {93--98},
  file = {KnutovHT10a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KnutovHT10a.pdf:PDF}
}
conference Zafra, A., Pechenizkiy, M. & Ventura, S. (2010) Reducing Dimensionality in Multiple Instance Learning with a Filter Method, In Proceedings of 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010), Part II, Lecture Notes in Computer Science, 6077, Springer, pp. 35-44.

BibTeX: 
@inproceedings{ZafraHAIS2010,
  author = {Amelia Zafra and Mykola Pechenizkiy and Sebastián Ventura},
  title = {Reducing Dimensionality in Multiple Instance Learning with a Filter Method},
  booktitle = {Proceedings of 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010), Part II},
  editor = {Emilio Corchado and Manuel Grana Romay and Alexandre Savio},
  publisher = {Springer},
  year = {2010},
  volume = {6077},
  pages = {35-44},
  doi = {http://doi.org/10.1007/978-3-642-13803-4_5},
  file = {ZafraHAIS10.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZafraHAIS10.pdf:PDF}
}
technical report Žliobaitė, I. & Pechenizkiy, M. (2010) Reference Framework for Handling Concept Drift: An Application Perspective Eindhoven University of Technology.

BibTeX: 
@techreport{ZliobaiteTECH2010,
  author = {Indrė Žliobaitė and Mykola Pechenizkiy},
  title = {Reference Framework for Handling Concept Drift: An Application Perspective},
  school = {Eindhoven University of Technology},
  year = {2010},
  file = {CD_applications_cases.pdf?attredirects=0:https//sites.google.com/site/zliobaite/CD_applications_cases.pdf?attredirects=0:PDF}
}
technical report Pechenizkiy, M., Ivannikov, A., Äyrämö, S. & Kärkkäinen, T. (2010) Towards Better Understanding and Control of CFB-Boilers: Review of Recent Research in Mining Time Series Data Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), University of Jyväskylä, 2010/2.

BibTeX: 
@techreport{PechenizkiyTechReport2010,
  author = {Pechenizkiy, M. and Ivannikov, A. and Äyrämö, S. and Kärkkäinen, T.},
  title = {Towards Better Understanding and Control of CFB-Boilers: Review of Recent Research in Mining Time Series Data},
  school = {Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), University of Jyväskylä},
  year = {2010},
  number = {2010/2}
}
poster Vasilyeva, E., Pechenizkiy, M., Tesanovic, A., Knutov, E. & De Bra, P. (2010) Towards EDM framework for Personalization of Information Services in RPM Systems, In Proceedings of the 3rd Conference on Educational Data Mining (EDM), pp. 331-332.

BibTeX: 
@inproceedings{VasilyevaEDM2010,
  author = {E. Vasilyeva and M. Pechenizkiy and A. Tesanovic and E. Knutov and De Bra, P.},
  title = {Towards EDM framework for Personalization of Information Services in RPM Systems},
  booktitle = {Proceedings of the 3rd Conference on Educational Data Mining (EDM)},
  year = {2010},
  pages = {331--332},
  file = {VasilyevaEDM2010.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaEDM2010.pdf:PDF}
}
book chapter Puuronen, S. & Pechenizkiy, M. (2010) "Towards the generic framework for utility considerations in data mining research", In Data Mining for Business Applications, Amsterdam: IOS Press, pp. 49-65.

BibTeX: 
@incollection{PuuronenBook2010,
  author = {Seppo Puuronen and Mykola Pechenizkiy},
  title = {Towards the generic framework for utility considerations in data mining research},
  booktitle = {Data Mining for Business Applications},
  publisher = {Amsterdam: IOS Press},
  year = {2010},
  pages = {49--65},
  pdf = {View.aspx?piid=18460:http//www.booksonline.iospress.nl/Content/View.aspx?piid=18460:PDF}  
}
journal Knutov, E., De Bra, P. & Pechenizkiy, M. (2009) AH 12 Years Later: a Comprehensive Survey of Adaptive Hypermedia Methods and Techniques, New Review of Hypermedia and Multimedia, 15(1), pp. 5-38.

BibTeX: 
@article{KnutovNRHM09,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {AH 12 Years Later: a Comprehensive Survey of Adaptive Hypermedia Methods and Techniques},
  journal = {New Review of Hypermedia and Multimedia},
  year = {2009},
  volume = {15},
  number = {1},
  pages = {5--38},
  doi = {http://doi.org/10.1080/13614560902801608},
  file = {KnutovNRHM09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KnutovNRHM09.pdf:PDF}
}
poster Knutov, E., De Bra, P. & Pechenizkiy, M. (2009) AH 12 years later: A Comprehensive Survey of Adaptive Hypermedia Methods and Techniques (Extended abstract), In Proceedings 21st Benelux Conference on Artificial Intelligence (BNAIC'09), pp. 339-341.

BibTeX: 
@inproceedings{KnutovBNAIC09,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {AH 12 years later: A Comprehensive Survey of Adaptive Hypermedia Methods and Techniques (Extended abstract)},
  booktitle = {Proceedings 21st Benelux Conference on Artificial Intelligence (BNAIC'09)},
  editor = {Toon Calders and Karl Tuyls and Mykola Pechenizkiy},
  year = {2009},
  pages = {339--341},
  url = {http://wwwis.win.tue.nl/bnaic2009/proc.html},
  file = {KnutovBNAIC09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KnutovBNAIC09.pdf:PDF}
}
workshop Calders, T., Kamiran, F. & Pechenizkiy, M. (2009) Building Classifiers with Independency Constraints, In Proceedings of IEEE International Conference on Data Mining ICDM'09 Workshops, IEEE Computer Society, pp. 13-18.

Abstract: In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise for example when the training data is collected from different sources with different labeling criteria or when the data is generated by a biased decision process. When a classifier is trained directly on such data, these undesirable dependencies will carry over to the classifier’s predictions. In order to tackle this problem, we study the classification with independency constraints problem: find an accurate model for which the predictions are independent from a given binary attribute. We propose two solutions for this problem and present an empirical validation.
BibTeX: 
@inproceedings{CaldersICDM09,
  author = {Toon Calders and Faisal Kamiran and Mykola Pechenizkiy},
  title = {Building Classifiers with Independency Constraints},
  booktitle = {Proceedings of IEEE International Conference on Data Mining ICDM'09 Workshops},
  publisher = {IEEE Computer Society},
  year = {2009},
  pages = {13--18},
  doi = {http://doi.org/10.1109/ICDMW.2009.83},
  file = {CaldersICDM09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/CaldersICDM09.pdf:PDF}
}
poster Žliobaitė, I., Bakker, J. & Pechenizkiy, M. (2009) Context Aware Sales Prediction. Extended abstract, In Proceedings 21st Benelux Conference on Artificial Intelligence (BNAIC'09), pp. 449-450.

BibTeX: 
@inproceedings{ZliobaiteBNAIC09,
  author = {Indrė Žliobaitė and Bakker, Jorn and Pechenizkiy, Mykola},
  title = {Context Aware Sales Prediction. Extended abstract},
  booktitle = {Proceedings 21st Benelux Conference on Artificial Intelligence (BNAIC'09)},
  editor = {Toon Calders and Karl Tuyls and Mykola Pechenizkiy},
  year = {2009},
  pages = {449--450},
  url = {http://wwwis.win.tue.nl/bnaic2009/proc.html},
  file = {ZliobaiteBNAIC09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZliobaiteBNAIC09.pdf:PDF}
}
editorial De Bra, P. & Pechenizkiy, M. (2009) Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques, In Proceedings of the 20th ACM conference on Hypertext and hypermedia (HT'09), ACM Press, pp. 387-388.

BibTeX: 
@inproceedings{DeBraHT09,
  author = {De Bra, Paul and Pechenizkiy, Mykola},
  title = {Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques},
  booktitle = {Proceedings of the 20th ACM conference on Hypertext and hypermedia (HT'09)},
  publisher = {ACM Press},
  year = {2009},
  pages = {387--388},
  doi = {http://doi.org/10.1145/1557914.1558003},
  file = {DeBraHT09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/DeBraHT09.pdf:PDF}
}
conference Tesanovic, A., Manev, G., Pechenizkiy, M. & Vasilyeva, E. (2009) eHealth personalization in the next generation RPM systems, In Proceedings of 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS'09), IEEE Computer Society, pp. 1-8.

BibTeX: 
@inproceedings{TesanovicCBMS09,
  author = {Tesanovic, A. and Manev, G. and Pechenizkiy, M. and Vasilyeva, E.},
  title = {eHealth personalization in the next generation RPM systems},
  booktitle = {Proceedings of 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS'09)},
  publisher = {IEEE Computer Society},
  year = {2009},
  pages = {1--8},
  doi = {http://doi.org/10.1109/CBMS.2009.5255383},
  file = {TesanovicCBMS09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TesanovicCBMS09.pdf:PDF}
}
poster Tesanovic, A., Manev, G., Pechenizkiy, M. & Vasilyeva, E. (2009) eHealth Personalization in the Next Gneration RPM Systems (Extended abstract), In Proceedings of 21st Benelux Conference on Artificial Intelligence (BNAIC'09), pp. 445-446.

BibTeX: 
@inproceedings{TesanovicBNAIC09,
  author = {Aleksandra Tesanovic and Goran Manev and Mykola Pechenizkiy and Ekaterina Vasilyeva},
  title = {eHealth Personalization in the Next Gneration RPM Systems (Extended abstract)},
  booktitle = {Proceedings of 21st Benelux Conference on Artificial Intelligence (BNAIC'09)},
  editor = {Toon Calders and Karl Tuyls and Mykola Pechenizkiy},
  year = {2009},
  pages = {445--446},
  url = {http://wwwis.win.tue.nl/bnaic2009/proc.html},
  file = {TesanovicBNAIC09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TesanovicBNAIC09.pdf:PDF}
}
conference Bakker, J. & Pechenizkiy, M. (2009) Food Wholesales Prediction: What is your Baseline?, In Foundations of Intelligent Systems - Proceedings of 18th International Symposium (ISMIS'09), Lecture Notes in Computer Science, 5722, Berlin: Springer, pp. 493-502.

BibTeX: 
@inproceedings{BakkerISMIS09,
  author = {Jorn Bakker and Mykola Pechenizkiy},
  title = {Food Wholesales Prediction: What is your Baseline?},
  booktitle = {Foundations of Intelligent Systems - Proceedings of 18th International Symposium (ISMIS'09)},
  editor = {J. Rauch, Z.W. Ras, P. Berka, T. Elomaa},
  publisher = {Berlin: Springer},
  year = {2009},
  volume = {5722},
  pages = {493--502},
  doi = {http://doi.org/10.1007/978-3-642-04125-9_52},
  file = {BakkerISMIS09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/BakkerISMIS09.pdf:PDF}
}
workshop Trčka, N. & Pechenizkiy, M. (2009) From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining, In Proceedings of Ninth International Conference on Intelligent Systems Design and Applications (ISDA'09), pp. 1114-1119.

Abstract: Educational process mining (EPM) aims at (i) constructing complete and compact educational process models that are able to reproduce all observed behavior (process model discovery), (ii) checking whether the modeled behavior (either pre-authored or discovered from data) matches the observed behavior (conformance checking), and (iii) projecting information extracted from the logs onto the model, to make the tacit knowledge explicit and facilitate better understanding of the process (process model extension). In this paper we propose a new domain-driven framework for EPM which assumes that a set of pattern templates can be predefined to focus the mining in a desired way and make it more effective and efficient. We illustrate the ideas behind our approach with examples of academic curricular modeling, mining, and conformance checking, using the student database of our department.
BibTeX: 
@inproceedings{TrckaISDA09,
  author = {Nikola Trčka and Mykola Pechenizkiy},
  title = {From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining},
  booktitle = {Proceedings of Ninth International Conference on Intelligent Systems Design and Applications (ISDA'09)},
  year = {2009},
  pages = {1114--1119},
  doi = {http://doi.org/10.1109/ISDA.2009.159},
  file = {TrckaISDA09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TrckaISDA09.pdf:PDF}
}
editorial Pechenizkiy, M. & Tsymbal, A. (2009) Guest Editorial for DKE Special Issue on "Biomedical Data Mining", Data & Knowledge Engineering, 68(12), pp. 1357-1358.

BibTeX: 
@article{PechenizkiyDKE09,
  author = {Mykola Pechenizkiy and Alexey Tsymbal},
  title = {Guest Editorial for DKE Special Issue on "Biomedical Data Mining"},
  journal = {Data & Knowledge Engineering},
  year = {2009},
  volume = {68},
  number = {12},
  pages = {1357-1358},
  doi = {http://doi.org/10.1016/j.datak.2009.07.001},
  file = {PechenizkiyDKE09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyDKE09.pdf:PDF}
}
workshop Bakker, J., Pechenizkiy, M., Žliobaitė, I., Ivannikov, A. & Kärkkäinen, T. (2009) Handling Outliers and Concept Drift in Online Mass Flow Prediction in CFB boilers, In Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (SensorKDD'09), ACM Press, pp. 13-22.

Abstract: In this paper we consider an application of data mining technology to the analysis of time series data from a pilot circulating fluidized bed (CFB) reactor. We focus on the problem of the online mass prediction in CFB boilers. We present a framework based on switching regression models depending on perceived changes in the data. We analyze three alternatives for change detection. Additionally, a noise canceling and a state determination and windowing mechanisms are used for improving the robustness of online prediction. We validate our ideas on real data collected from the pilot CFB boiler.
BibTeX: 
@inproceedings{BakkerSensorsKDD09,
  author = {Bakker, J. and Pechenizkiy, M. and Žliobaitė, I. and Ivannikov, A. and Kärkkäinen, T.},
  title = {Handling Outliers and Concept Drift in Online Mass Flow Prediction in CFB boilers},
  booktitle = {Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (SensorKDD'09)},
  publisher = {ACM Press},
  year = {2009},
  pages = {13--22},
  doi = {http://doi.org/10.1145/1601966.1601972},
  file = {BakkerSensorsKDD09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/BakkerSensorsKDD09.pdf:PDF}
}
conference Žliobaitė, I., Bakker, J. & Pechenizkiy, M. (2009) OMFP: an Approach for Online Mass Flow Prediction in CFB Boilers, In Proceedings of 12th International Conference on Discovery Science (DS'09), Lecture Notes in Computer Science, 5808, Berlin: Springer, pp. 272-286.

BibTeX: 
@inproceedings{ZliobaiteDS09,
  author = {Indrė Žliobaitė and Jorn Bakker and Mykola Pechenizkiy},
  title = {OMFP: an Approach for Online Mass Flow Prediction in CFB Boilers},
  booktitle = {Proceedings of 12th International Conference on Discovery Science (DS'09)},
  editor = {J. Gama and V. Santos Costa and A.M. Jorge and P.B. Brazdil},
  publisher = {Berlin: Springer},
  year = {2009},
  volume = {5808},
  pages = {272--286},
  doi = {http://doi.org/10.1007/978-3-642-04747-3_22},
  file = {ZliobaiteDS09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZliobaiteDS09.pdf:PDF}
}
conference Ivannikov, A., Pechenizkiy, M., Bakker, J., Leino, T., Jegoroff, M., Kärkkäinen, T. & Äyrämö, S. (2009) Online Mass Flow Prediction in CFB Boilers, In Advances in Data Mining. Applications and Theoretical Aspects, Proceedings of 9th Industrial Conference on Data Mining (ICDM'09), Lecture Notes in Computer Science, 5633, Springer, pp. 206-219.

BibTeX: 
@inproceedings{IvannikovICDM09,
  author = {Andriy Ivannikov and Mykola Pechenizkiy and Jorn Bakker and Timo Leino and Mikko Jegoroff and Tommi Kärkkäinen and Sami Äyrämö},
  title = {Online Mass Flow Prediction in CFB Boilers},
  booktitle = {Advances in Data Mining. Applications and Theoretical Aspects, Proceedings of 9th Industrial Conference on Data Mining (ICDM'09)},
  editor = {Petra Perner},
  publisher = {Springer},
  year = {2009},
  volume = {5633},
  pages = {206--219},
  doi = {http://doi.org/10.1007/978-3-642-03067-3_17},
  file = {IvannikovICDM09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/IvannikovICDM09.pdf:PDF}
}
journal Pechenizkiy, M., Bakker, J., Žliobaitė, I., Ivannikov, A. & Kärkkäinen, T. (2009) Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift, SIGKDD Explorations, 11(2), pp. 109-116.

BibTeX: 
@article{PechenizkiySIGKDDExpl09,
  author = {Pechenizkiy, M. and Bakker, J. and Žliobaitė, I. and Ivannikov, A. and Kärkkäinen, T.},
  title = {Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift},
  journal = {SIGKDD Explorations},
  year = {2009},
  volume = {11},
  number = {2},
  pages = {109-116},
  doi = {http://www.sigkdd.org/explorations/issue.php?volume=11\&issue=2\&year=2009\&month=12},
  file = {PechenizkiySIGKDDExpl09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiySIGKDDExpl09.pdf:PDF}
}
journal Tourunen, A., Ivannikov, A., Saastamoinen, J., Kärkkäinen, T., Nevalainen, H. & Pechenizkiy, M. (2009) Online Method to Characterize Char Loading in Fluidized Bed Combustion, Journal of Materials Engineering and Technology, 1(2), pp. 191-203.

BibTeX: 
@article{Tourunen2009,
  author = {A. Tourunen and Andriy Ivannikov and J. Saastamoinen and Tommi Kärkkäinen and H. Nevalainen and M. Pechenizkiy},
  title = {Online Method to Characterize Char Loading in Fluidized Bed Combustion},
  journal = {Journal of Materials Engineering and Technology},
  year = {2009},
  volume = {1},
  number = {2},
  pages = {191--203}
}
conference Dekker, G.W., Pechenizkiy, M. & Vleeshouwers, J.M. (2009) Predicting Students Drop Out: a Case Study, In Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09), pp. 41-50.

BibTeX: 
@inproceedings{DekkerEDM09,
  author = {G.W. Dekker and M. Pechenizkiy and J.M. Vleeshouwers},
  title = {Predicting Students Drop Out: a Case Study},
  booktitle = {Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09)},
  editor = {T. Barnes and M. Desmarais and C. Romero and S. Ventura},
  year = {2009},
  pages = {41--50},
  url = {http://www.educationaldatamining.org/EDM2009/index.php?page=proceedings},
  file = {DekkerEDM09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/DekkerEDM09.pdf:PDF}
}
editorial Calders, T., Tuyls, K. & Pechenizkiy, M. (2009) Preface to the Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC'09), pp. iii.

BibTeX: 
@inproceedings{CaldersBNAICEdit09,
  author = {Toon Calders and Karl Tuyls and Mykola Pechenizkiy},
  title = {Preface to the Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC'09)},
  year = {2009},
  pages = {iii},
  url = {http://wwwis.win.tue.nl/bnaic2009/proc.html},
  file = {bnaic2009_paper_pref.pdf:http//wwwis.win.tue.nl/bnaic2009/papers/bnaic2009_paper_pref.pdf:PDF}
}
proceedings Calders, T., Tuyls, K. & Pechenizkiy, M. (Eds.) (2009) Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC'09).

BibTeX: 
@misc{CaldersBNAIC09,,
  title = {Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC'09)},
  year = {2009},
  doi = {http://wwwis.win.tue.nl/bnaic2009/proc.html},
  file = {bnaic2009_proceedings.pdf:http//wwwis.win.tue.nl/bnaic2009/papers/bnaic2009_proceedings.pdf:PDF}
}
proceedings De Bra, P. & Pechenizkiy, M. (Eds.) (2009) Proceedings of the Workshop on "Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques" (DAH'09) @ ACM HT'09 Conference.

BibTeX: 
@misc{DeBraHTEdit09,,
  title = {Proceedings of the Workshop on "Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques" (DAH'09) @ ACM HT'09 Conference},
  publisher = {CEUR-WS.org},
  year = {2009},
  volume = {473},
  doi = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-473/},
  file = {DeBraHTEdit09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/DeBraHTEdit09.pdf:PDF}
}
conference Pechenizkiy, M., Trčka, N., Vasilyeva, E., van der Aalst, W.M.P. & De Bra, P. (2009) Process Mining Online Assessment Data, In Proceedings of 2nd International Conference on Educational Data Mining (EDM'09), pp. 279-288.

BibTeX: 
@inproceedings{PechenizkiyEDM09,
  author = {Mykola Pechenizkiy and Nikola Trčka and Ekaterina Vasilyeva and W.M.P. van der Aalst and De Bra, Paul},
  title = {Process Mining Online Assessment Data},
  booktitle = {Proceedings of 2nd International Conference on Educational Data Mining (EDM'09)},
  editor = {T. Barnes and M. Desmarais and C. Romero and S. Ventura},
  year = {2009},
  pages = {279--288},
  url = {http://www.educationaldatamining.org/EDM2009/index.php?page=proceedings},
  file = {PechenizkiyEDM09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyEDM09.pdf:PDF}
}
workshop Žliobaitė, I., Bakker, J. & Pechenizkiy, M. (2009) Towards Context Aware Food Sales Prediction, In Proceedings of IEEE International Conference on Data Mining (ICDM'09) Workshops, IEEE Computer Society, pp. 94-99.

Abstract: Sales prediction is a complex task because of a large number of factors affecting the demand. We present a context aware sales prediction approach, which selects the base predictor depending on the structural properties of the historical sales. In the experimental part we show that there exist product subsets on which, using this strategy, it is possible to outperform naive methods. We also show the dependencies between product categorization accuracies and sales prediction accuracies. A case study of a food wholesaler indicates that moving average prediction can be outperformed by intelligent methods, if proper categorization is in place, which appears to be a difficult task.
BibTeX: 
@inproceedings{ZliobaiteICDM09,
  author = {Indrė Žliobaitė and Bakker, Jorn and Pechenizkiy, Mykola},
  title = {Towards Context Aware Food Sales Prediction},
  booktitle = {Proceedings of IEEE International Conference on Data Mining (ICDM'09) Workshops},
  editor = {Yücel Saygin and Jeffrey Xu Yu and Hillol Kargupta and Wei Wang and Sanjay Ranka and Philip S. Yu and Xindong Wu},
  publisher = {IEEE Computer Society},
  year = {2009},
  pages = {94--99},
  doi = {http://doi.org/10.1109/ICDMW.2009.60},
  file = {ZliobaiteDDDM09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/ZliobaiteDDDM09.pdf:PDF}
}
workshop Tesanovic, A., Manev, G., Pechenizkiy, M. & Vasilyeva, E. (2009) Towards the Second Order Adaptation in the Next Generation Remote Patient Management Systems, In Proceedings of the Workshop on Personalisation for eHealth (Pers4eHealth'09), pp. 34-40.

BibTeX: 
@inproceedings{TesanovicAIME09,
  author = {Aleksandra Tesanovic and Goran Manev and Mykola Pechenizkiy and Ekaterina Vasilyeva},
  title = {Towards the Second Order Adaptation in the Next Generation Remote Patient Management Systems},
  booktitle = {Proceedings of the Workshop on Personalisation for eHealth (Pers4eHealth'09)},
  editor = {F. Grasso and C. Paris},
  year = {2009},
  pages = {34--40},
  url = {http://www.csc.liv.ac.uk/ floriana/Pers4eHealth09/Programme.html},
  file = {TesanovicAIME09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TesanovicAIME09.pdf:PDF}
}
conference Calders, T., Günther, C.W., Pechenizkiy, M. & Rozinat, A. (2009) Using Minimum Description Length for Process Mining, In Proceedings of the 2009 ACM symposium on Applied Computing (SAC'09), ACM Press, pp. 1451-1455.

Abstract: In the field of process mining, the goal is to automatically extract process models from event logs. Recently, many algorithms have been proposed for this task. For comparing these models, different quality measures have been proposed. Most of these measures, however, have several disadvantages; they are model-dependent, assume that the model that generated the log is known, or need negative examples of event sequences. In this paper we propose a new measure, based on the minimal description length principle, to evaluate the quality of process models that does not have these disadvantages. To illustrate the properties of the new measure we conduct experiments and discuss the trade-off between model complexity and compression.
BibTeX: 
@inproceedings{CaldersSAC09,
  author = {Toon Calders and Christian W. Günther and Mykola Pechenizkiy and Anne Rozinat},
  title = {Using Minimum Description Length for Process Mining},
  booktitle = {Proceedings of the 2009 ACM symposium on Applied Computing (SAC'09)},
  publisher = {ACM Press},
  year = {2009},
  pages = {1451--1455},
  doi = {http://doi.org/10.1145/1529282.1529606},
  file = {CaldersSAC09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/CaldersSAC09.pdf:PDF}
}
workshop Knutov, E., De Bra, P. & Pechenizkiy, M. (2009) Versioning in Adaptive Hypermedia, In Proceedings of the 1st DAH'2009 Workshop on Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques, ACM Hypertext Conference (HT'09), CEUR Workshop Proceedings, 473, Aachen: CEUR-WS.org., pp. 61-71.

BibTeX: 
@inproceedings{KnutovDAH09,
  author = {Evgeny Knutov and De Bra, Paul and Mykola Pechenizkiy},
  title = {Versioning in Adaptive Hypermedia},
  booktitle = {Proceedings of the 1st DAH'2009 Workshop on Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques, ACM Hypertext Conference (HT'09)},
  editor = {Mykola Pechenizkiy and Paul De Bra},
  publisher = {Aachen: CEUR-WS.org.},
  year = {2009},
  volume = {473},
  pages = {61--71},
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-473/},
  file = {KnutovDAH09.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/KnutovDAH09.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M. & De Bra, P. (2008) Adaptation of Elaborated Feedback in e-Learning, In Proceedings of International Conference on Adaptive Hypermedia (AH'08), pp. 235-244.

BibTeX: 
@inproceedings{VasilyevaAH08,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and De Bra, Paul},
  title = {Adaptation of Elaborated Feedback in e-Learning},
  booktitle = {Proceedings of International Conference on Adaptive Hypermedia (AH'08)},
  year = {2008},
  pages = {235-244},
  doi = {http://doi.org/10.1007/978-3-540-70987-9_26},
  file = {VasilyevaACH08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaACH08.pdf:PDF}
}
poster De Bra, P., Smits, D., Pechenizkiy, M. & Vasilyeva, E. (2008) Adaptivity in GRAPPLE: Adaptation in Any Way You Like, In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn'08), AACE, pp. 3654-3659.

BibTeX: 
@inproceedings{DeBraELearn08,
  author = {De Bra, Paul and David Smits and Mykola Pechenizkiy and Ekaterina Vasilyeva},
  title = {Adaptivity in GRAPPLE: Adaptation in Any Way You Like},
  booktitle = {Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn'08)},
  publisher = {AACE},
  year = {2008},
  pages = {3654--3659},
  url = {http://www.editlib.org/p/30194},
  file = {DeBraELearn08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/DeBraELearn08.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M. & De Bra, P. (2008) Analysis of Feedback Authoring Possibilities in Web-based Learning Systems, In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn'08), AACE, pp. 3981-3986.

BibTeX: 
@inproceedings{VasilyevaELEARN08Short,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and De Bra, Paul},
  title = {Analysis of Feedback Authoring Possibilities in Web-based Learning Systems},
  booktitle = {Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn'08)},
  publisher = {AACE},
  year = {2008},
  pages = {3981--3986},
  url = {http://www.editlib.org/p/30244},
  file = {VasilyevaELEARN08Short.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaELEARN08Short.pdf:PDF}
}
conference Hendrix, M., De Bra, P., Pechenizkiy, M., Smits, D. & Cristea, A.I. (2008) Defining Adaptation in a Generic Multi Layer Model: CAM: The GRAPPLE Conceptual Adaptation Model, In Times of Convergence. Technologies Across Learning Contexts, Proceedings of 3rd European Conference on Technology Enhanced Learning (EC-TEL'08), pp. 132-143.

BibTeX: 
@inproceedings{HendrixECTEL08,
  author = {Maurice Hendrix and De Bra, Paul and Mykola Pechenizkiy and David Smits and Alexandra I. Cristea},
  title = {Defining Adaptation in a Generic Multi Layer Model: CAM: The GRAPPLE Conceptual Adaptation Model},
  booktitle = {Times of Convergence. Technologies Across Learning Contexts, Proceedings of 3rd European Conference on Technology Enhanced Learning (EC-TEL'08)},
  year = {2008},
  pages = {132--143},
  doi = {http://doi.org/10.1007/978-3-540-87605-2_16},
  file = {HendrixECTEL08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/HendrixECTEL08.pdf:PDF}
}
book chapter Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2008) "Does Relevance Matter to Data Mining Research?", In Data Mining: Foundations and Practice, Vol. 118, Springer: Berlin/Heidelberg, pp. 251-275.

BibTeX: 
@incollection{PechenizkiyDMFP08,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Alexey Tsymbal},
  title = {Does Relevance Matter to Data Mining Research?},
  booktitle = {Data Mining: Foundations and Practice},
  publisher = {Springer: Berlin/Heidelberg},
  year = {2008},
  volume = {118},
  pages = {251--275},
  doi = {http://doi.org/10.1007/978-3-540-78488-3_15},
  pdf = {PechenizkiyDMFP08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyDMFP08.pdf:PDF}  
}
journal Tsymbal, A., Pechenizkiy, M., Cunningham, P. & Puuronen, S. (2008) Dynamic Integration of Classifiers for Handling Concept Drift, Information Fusion, Special Issue on Applications of Ensemble Methods, 9(1), pp. 56-68.

BibTeX: 
@article{TsymbalIF08,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham and Seppo Puuronen},
  title = {Dynamic Integration of Classifiers for Handling Concept Drift},
  journal = {Information Fusion, Special Issue on Applications of Ensemble Methods},
  year = {2008},
  volume = {9},
  number = {1},
  pages = {56--68},
  doi = {http://doi.org/10.1016/j.inffus.2006.11.002},
  file = {TsymbalIF08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalIF08.pdf:PDF}
}
conference Puuronen, S., Pechenizkiy, M. & Tsymbal, A. (2008) Effectiveness of Local Feature Selection in Ensemble Learning for Prediction of Antimicrobial Resistance, In Proceedings of 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS'08), IEEE Computer Society, pp. 632-637.

Abstract: In the real world concepts are often not stable but change over time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift (CD), complicates the task of learning a robust model. Different ensemble learning (EL) approaches (that instead of learning a single classifier try to learn and maintain a set of classifiers over time) have been shown to perform reasonably well in the presence of concept drift. In this paper we study how much local feature selection (FS) can improve ensemble performance for data with concept drift. Our results show that FS may improve the performance of different EL strategies, yet being more important for EL with static integration of classifiers like (weighted) voting. Further, the improvement of EL due to FS can be explained by its effect on the accuracy and diversity of base classifiers. The results also provide some additional evidence that diversity can be better utilized with the dynamic integration of classifiers.
BibTeX: 
@inproceedings{PuuronenCBMS08,
  author = {Seppo Puuronen and Mykola Pechenizkiy and Alexey Tsymbal},
  title = {Effectiveness of Local Feature Selection in Ensemble Learning for Prediction of Antimicrobial Resistance},
  booktitle = {Proceedings of 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS'08)},
  publisher = {IEEE Computer Society},
  year = {2008},
  pages = {632--637},
  doi = {http://doi.org/10.1109/CBMS.2008.22},
  file = {PuuronenCBMS08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PuuronenCBMS08.pdf:PDF}
}
workshop Meulstee, P. & Pechenizkiy, M. (2008) Food Sales Prediction: "If Only It Knew What We Know", In Proceedings of IEEE International Conference on Data Mining (ICDM'08) Workshops, pp. 134-143.

BibTeX: 
@inproceedings{MeulsteeICDM08,
  author = {Meulstee, P. and Pechenizkiy, M.},
  title = {Food Sales Prediction: "If Only It Knew What We Know"},
  booktitle = {Proceedings of IEEE International Conference on Data Mining (ICDM'08) Workshops},
  year = {2008},
  pages = {134--143},
  doi = {http://doi.org/10.1109/ICDMW.2008.128},
  file = {MeulsteeDDDM08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MeulsteeDDDM08.pdf:PDF}
}
poster De Bra, P., Pechenizkiy, M., van der Sluijs, K. & Smits, D. (2008) GRAPPLE: Integrating Adaptive Learning into Learning Management Systems, In Proceedings of International Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA'08), AACE Press, pp. 5183-5188.

BibTeX: 
@inproceedings{DeBraEDMEDIA08,
  author = {De Bra, Paul and Mykola Pechenizkiy and Kees van der Sluijs and David Smits},
  title = {GRAPPLE: Integrating Adaptive Learning into Learning Management Systems},
  booktitle = {Proceedings of International Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA'08)},
  publisher = {AACE Press},
  year = {2008},
  pages = {5183--5188},
  url = {http://www.editlib.org/?fuseaction=Reader.PrintAbstract&paper_id=29093},
  file = {DeBraEDMEDIA08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/DeBraEDMEDIA08.pdf:PDF}
}
conference Vasilyeva, E., De Bra, P. & Pechenizkiy, M. (2008) Immediate Elaborated Feedback Personalization in Online Assessment, In Times of Convergence. Technologies Across Learning Contexts, Proceedings of 3rd European Conference on Technology Enhanced Learning (EC-TEL'08), pp. 449-460.

BibTeX: 
@inproceedings{VasilyevaECTEL08,
  author = {Ekaterina Vasilyeva and De Bra, Paul and Mykola Pechenizkiy},
  title = {Immediate Elaborated Feedback Personalization in Online Assessment},
  booktitle = {Times of Convergence. Technologies Across Learning Contexts, Proceedings of 3rd European Conference on Technology Enhanced Learning (EC-TEL'08)},
  year = {2008},
  pages = {449--460},
  doi = {http://doi.org/10.1007/978-3-540-87605-2_50},
  file = {VasilyevaECTEL08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaECTEL08.pdf:PDF}
}
conference Pechenizkiy, M., Calders, T., Vasilyeva, E. & De Bra, P. (2008) Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study, In Proceedings of the 2nd Conference on Educational Data Mining (EDM'08), pp. 187-191.

BibTeX: 
@inproceedings{PechenizkiyEDM08,
  author = {Mykola Pechenizkiy and Toon Calders and Ekaterina Vasilyeva and De Bra, Paul},
  title = {Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study},
  booktitle = {Proceedings of the 2nd Conference on Educational Data Mining (EDM'08)},
  year = {2008},
  pages = {187--191},
  url = {http://www.educationaldatamining.org/EDM2008/index.php?page=proceedings},
  file = {PechenizkiyEDM08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyEDM08.pdf:PDF}
}
editorial Puuronen, S., Pechenizkiy, M. & Tsymbal, A. (2008) Preface to the Proceedings of 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS'08), IEEE Computer Society, pp. xv.

BibTeX: 
@inproceedings{PuuronenCBMSPref08,
  author = {Seppo Puuronen and Mykola Pechenizkiy and Alexey Tsymbal},
  title = {Preface to the Proceedings of 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS'08)},
  publisher = {IEEE Computer Society},
  year = {2008},
  pages = {xv},
  doi = {http://doi.org/10.1109/CBMS.2008.4},
  file = {PuuronenCBMSPref08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PuuronenCBMSPref08.pdf:PDF}
}
proceedings Puuronen, S., Pechenizkiy, M., Tsymbal, A. & Lee, D.-J. (Eds.) (2008) Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS'08).

BibTeX: 
@misc{PuuronenCMBSProc08,,
  title = {Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS'08)},
  publisher = {IEEE Computer Society},
  year = {2008},
  doi = {http://doi.org/10.1109/CBMS.2008.3}
}
edited volume Bridewell, W., Calders, T., Medeiros, A.K., Kramer, S., Pechenizkiy, M. & Todorovski, L. (Eds.) (2008) Proceedings of the International Workshop on Induction of Process Models (IPM'08) @ ECML/PKDD 2008.

BibTeX: 
@book{BridewellIPM08,,
  title = {Proceedings of the International Workshop on Induction of Process Models (IPM'08) @ ECML/PKDD 2008},
  year = {2008},
  doi = {http://wwwkramer.in.tum.de/ipm08/program.html}
}
conference Vasilyeva, E., De Bra, P., Pechenizkiy, M. & Puuronen, S. (2008) Tailoring Feedback in Online Assessment: Influence of Learning Styles on the Feedback Preferences and Elaborated Feedback Effectiveness, In Proceedings of 8th IEEE International Conference on Advanced Learning Technologies (ICALT'08), pp. 834-838.

Abstract: Design of feedback is a critical issue of online assessment development within Web-based Learning Systems (WBLSs). This paper examines the potential possibilities of tailoring the feedback that is presented to a student as a result of his/her preferences and responses to questions of an online test with respect to the individual learning styles (LS). The paper briefly reviews the main types of feedback that can be presented during online assessment and discusses the challenges in authoring and tailoring of feedback in WBLSs. We report the results of some recent experiments organized as online assessment of students through multiple-choice quizzes in which students were able to request different kinds of feedback for the answered questions. The experimental results have confirmed that LS have a significant influence on (1) the feedback preferences (with regard to response certitude and correctness) of students and (2) the effectiveness of elaborated feedback (EF), i.e. improving studentspsila performance during the test.
BibTeX: 
@inproceedings{VasilyevaICALT08,
  author = {Ekaterina Vasilyeva and De Bra, Paul and Mykola Pechenizkiy and Seppo Puuronen},
  title = {Tailoring Feedback in Online Assessment: Influence of Learning Styles on the Feedback Preferences and Elaborated Feedback Effectiveness},
  booktitle = {Proceedings of 8th IEEE International Conference on Advanced Learning Technologies (ICALT'08)},
  year = {2008},
  pages = {834-838},
  doi = {http://doi.org/10.1109/ICALT.2008.66},
  file = {VasilyevaICALT08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaICALT08.pdf:PDF}
}
conference Vasilyeva, E., De Bra, P. & Pechenizkiy, M. (2008) Tailoring of Feedback in Online Assessment: Lessons Learnt, In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn'08), AACE, pp. 518-525.

BibTeX: 
@inproceedings{VasilyevaELEARN08Full,
  author = {Ekaterina Vasilyeva and De Bra, Paul and Mykola Pechenizkiy},
  title = {Tailoring of Feedback in Online Assessment: Lessons Learnt},
  booktitle = {Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn'08)},
  publisher = {AACE},
  year = {2008},
  pages = {518--525},
  url = {http://www.editlib.org/p/29654},
  file = {VasilyevaELEARN08Full.pdf:http//wwwis.win.tue.nl/ mpechen/publications/pubs/VasilyevaELEARN08Full.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M. & De Bra, P. (2008) Tailoring of Feedback in Web-Based Learning: The Role of Response Certitude in the Assessment, In Proceedings of 9th International Conference (ITS'08), Lecture Notes in Computer Science, 5091, Springer, pp. 771-773.

BibTeX: 
@inproceedings{VasilyevaITS08,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and De Bra, Paul},
  title = {Tailoring of Feedback in Web-Based Learning: The Role of Response Certitude in the Assessment},
  booktitle = {Proceedings of 9th International Conference (ITS'08)},
  editor = {Beverly Park Woolf and Esma A\imeur and Roger Nkambou and Susanne P. Lajoie},
  publisher = {Springer},
  year = {2008},
  volume = {5091},
  pages = {771-773},
  doi = {http://doi.org/10.1007/978-3-540-69132-7_104},
  file = {VasilyevaITS08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaITS08.pdf:PDF}
}
journal Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2008) Towards more Relevance-Oriented Data Mining Research, Intelligent Data Analysis, 12(2), pp. 237-249.

BibTeX: 
@article{PechenizkiyIDA08,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Alexey Tsymbal},
  title = {Towards more Relevance-Oriented Data Mining Research},
  journal = {Intelligent Data Analysis},
  year = {2008},
  volume = {12},
  number = {2},
  pages = {237--249},
  doi = {http://iospress.metapress.com/content/7115263j705612n1/},
  file = {PechenizkiyIDA08.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyIDA08.pdf:PDF}
}
workshop Pechenizkiy, M. & Calders, T. (2007) A Framework for Guiding the Museum Tour Personalization, In Proceedings of International Workshop on Personalization Enhanced Access to Cultural Heritage (PATCH'07) @ UM 2007 Conference, IIT, NCSR Demokritos, pp. 11-28.

BibTeX: 
@inproceedings{PechenizkiyPATCH07,
  author = {Mykola Pechenizkiy and Toon Calders},
  title = {A Framework for Guiding the Museum Tour Personalization},
  booktitle = {Proceedings of International Workshop on Personalization Enhanced Access to Cultural Heritage (PATCH'07) @ UM 2007 Conference},
  editor = {L.M. Aroyo and T. Kuflik and O. Stock and M. Zancanaro},
  publisher = {IIT, NCSR Demokritos},
  year = {2007},
  pages = {11--28},
  file = {PechenizkiyPATCH07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyPATCH07.pdf:PDF}
}
workshop Vasilyeva, E., Pechenizkiy, M. & De Bra, P. (2007) Adaptation of Feedback in e-Learning System at Individual and Group Level, In Proceedings of International Workshop on Personalization in E-Learning Environments at Individual and Group Level (PING'07) @ UM 2007 Conference, pp. 49-56.

BibTeX: 
@inproceedings{VasilyevaPING07,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and De Bra, Paul},
  title = {Adaptation of Feedback in e-Learning System at Individual and Group Level},
  booktitle = {Proceedings of International Workshop on Personalization in E-Learning Environments at Individual and Group Level (PING'07) @ UM 2007 Conference},
  editor = {Peter Brusilovsky and Maria Grigoriadou and Kyparissia Papanikolaou},
  year = {2007},
  pages = {49--56},
  file = {PING07-proceedings.pdf:http//hermis.di.uoa.gr/PeLEIGL/PING07-proceedings.pdf:PDF}
}
journal Pechenizkiy, M., Tsymbal, A., Puuronen, S. & Patterson, D.W. (2007) Feature Extraction for Dynamic Integration of Classifiers, Fundamenta Informaticae, 77(3), pp. 243-275.

BibTeX: 
@article{PechenizkiyFI07,
  author = {Mykola Pechenizkiy and Alexey Tsymbal and Seppo Puuronen and David W. Patterson},
  title = {Feature Extraction for Dynamic Integration of Classifiers},
  journal = {Fundamenta Informaticae},
  year = {2007},
  volume = {77},
  number = {3},
  pages = {243--275},
  doi = {http://iospress.metapress.com/index/E1N6040726164RV2.pdf},
  file = {PechenizkiyFI07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyFI07.pdf:PDF}
}
journal Vasilyeva, E., Puuronen, S., Pechenizkiy, M. & Rasanen, P. (2007) Feedback Adaptation in Web-Based Learning Systems, International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), 17(4/5), pp. 337-357.

BibTeX: 
@article{VasilyevaIJCEELL07,
  author = {Ekaterina Vasilyeva and Seppo Puuronen and Mykola Pechenizkiy and Pekka Rasanen},
  title = {Feedback Adaptation in Web-Based Learning Systems},
  journal = {International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL)},
  year = {2007},
  volume = {17},
  number = {4/5},
  pages = {337--357},
  doi = {http://doi.org/10.1504/IJCEELL.2007.015046},
  file = {VasilyevaIJCEELL07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaIJCEELL07.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M., Gavrilova, T. & Puuronen, S. (2007) Personalization of Immediate Feedback to Learning Styles, In Proceedings of 7th IEEE International Conference on Advanced Learning Technologies (ICALT'07), IEEE Computer Society, pp. 622-624.

BibTeX: 
@inproceedings{VasilyevaICALT07,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and Tatiana Gavrilova and Seppo Puuronen},
  title = {Personalization of Immediate Feedback to Learning Styles},
  booktitle = {Proceedings of 7th IEEE International Conference on Advanced Learning Technologies (ICALT'07)},
  editor = {Michael Spector and Demetrios G. Sampson and Toshio Okamoto and Kinshuk and Stefano A. Cerri and Maomi Ueno and Akihiro Kashihara},
  publisher = {IEEE Computer Society},
  year = {2007},
  pages = {622--624},
  doi = {http://doi.org/10.1109/ICALT.2007.200},
  file = {VasilyevaICALT07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaICALT07.pdf:PDF}
}
editorial Romero, C., Pechenizkiy, M., Calders, T., Viola, S.R. & Assche, F.V. (2007) Preface to the Proceedings of International Workshop on Applying Data Mining in e-Learning (ADML'07) @ ECTEL 2007 Conference, CEUR-WS.org, pp. 1.

BibTeX: 
@inproceedings{RomeroADML07Edit,
  author = {Cristobal Romero and Mykola Pechenizkiy and Toon Calders and Silvia R. Viola and Frans Van Assche},
  title = {Preface to the Proceedings of International Workshop on Applying Data Mining in e-Learning (ADML'07) @ ECTEL 2007 Conference},
  publisher = {CEUR-WS.org},
  year = {2007},
  pages = {1},
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-305/},
  file = {RomeroADML07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/RomeroADML07.pdf:PDF}
}
proceedings Romero, C., Pechenizkiy, M., Calders, T., Viola, S.R. & Assche, F.V. (Eds.) (2007) Proceedings of International Workshop on Applying Data Mining in e-Learning (ADML'07) @ ECTEL 2007 Conference.

BibTeX: 
@misc{RomeroADML07,,
  title = {Proceedings of International Workshop on Applying Data Mining in e-Learning (ADML'07) @ ECTEL 2007 Conference},
  publisher = {CEUR-WS.org},
  year = {2007},
  doi = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-305/},
  file = {RomeroADML07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/RomeroADML07.pdf:PDF}
}
proceedings Beck, J.E., Calders, T., Pechenizkiy, M. & Viola, S.R. (Eds.) (2007) Proceedings of the Workshop on Educational Data Mining @ ICALT 2007 (EDM@ICALT'07).

BibTeX: 
@misc{BeckEDMT07,,
  title = {Proceedings of the Workshop on Educational Data Mining @ ICALT 2007 (EDM@ICALT'07)},
  publisher = {CEUR-WS.org},
  year = {2007},
  doi = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-305/},
  file = {adml.pdf:http//www.educationaldatamining.org/adml.pdf:PDF}
}
conference Mertik, M., Pechenizkiy, M., Stiglic, G. & Kokol, P. (2007) Using Cellular Automata for Feature Construction - Preliminary Study, In Proceedings of Inaugural IEEE-IES Digital EcoSystems and Technologies Conference (DEST'07), pp. 479-484.

Abstract: When first faced with a learning task, it is often not clear what a good representation of the training data should look like. We are often forced to create some set of features that appear plausible, without any strong confidence that they will yield superior learning. Beside, we often do not have any prior knowledge of what learning method is the best to apply, and thus often try multiple methods in an attempt to find the one that performs best. This paper describes a new method and its preliminary study for constructing features based on cellular automata (CA). Our approach uses self-organisation ability of cellular automata by constructing features being most efficient for making predictions. We present and compare the CA approach with standard genetic algorithm (GA) which both use genetic programming (GP) for constructing the features. We show and discuss some interesting properties of using CA approach in our preliminary experimental study by constructing features on synthetically generated dataset and benchmark datasets from the UCI machine learning repository. Based on the interesting results, we conclude with directions and orientation of the future work with ideas of applicability of CA approach in the feature.
BibTeX: 
@inproceedings{MertikEcoSystems07,
  author = {Mertik, M. and Pechenizkiy, M. and Stiglic, G. and Kokol, P.},
  title = {Using Cellular Automata for Feature Construction - Preliminary Study},
  booktitle = {Proceedings of Inaugural IEEE-IES Digital EcoSystems and Technologies Conference (DEST'07)},
  year = {2007},
  pages = {479--484},
  doi = {http://doi.org/10.1109/DEST.2007.372023},
  file = {MetrikEcoSystems07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/MetrikEcoSystems07.pdf:PDF}
}
editorial Beck, J.E., Calders, T., Pechenizkiy, M. & Viola, S.R. (2007) Workshop on Educational Data Mining @ ICALT 2007 (EDM@ICALT'07), In Proceedings of 7th IEEE International Conference on Advanced Learning Technologies (ICALT'07), IEEE Computer Society, pp. 933-934.

Abstract: The educational data mining workshop1 held in conjunction with the 7 IEEE International Conference on Advanced Learning Technologies (ICALT) in Niigata, Japan on July 18-20, 2007. EDM@ICALT07 continues the series of Workshops organized by the International Working Group on Educational Data Mining during 2007. For upcoming events in educational data mining and for information on past workshops. Recently, the increase in dissemination of interactive learning environments has allowed the collection of huge amounts of data. An effective way of discovering new knowledge from large and complex data sets is data mining. The EDM workshop aimed for papers that study how to apply data mining to analyze data generated by learning systems or experiments, as well as how discovered information can be used to improve adaptation and personalization. Interesting problems data mining can help to solve are: determining what are common learning styles or strategies, predicting the knowledge and interests of a user based on past behavior, partitioning a heterogeneous group of users into homogeneous clusters, etc.
BibTeX: 
@inproceedings{BeckEDMICALT07,
  author = {Beck, J. E. and Calders, T. and Pechenizkiy, M. and Viola, S. R.},
  title = {Workshop on Educational Data Mining @ ICALT 2007 (EDM@ICALT'07)},
  booktitle = {Proceedings of 7th IEEE International Conference on Advanced Learning Technologies (ICALT'07)},
  publisher = {IEEE Computer Society},
  year = {2007},
  pages = {933--934},
  doi = {http://doi.org/10.1109/ICALT.2007.286},
  file = {BeckICALT07.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/BeckICALT07.pdf:PDF}
}
conference Pechenizkiy, M., Tsymbal, A., Puuronen, S. & Pechenizkiy, O. (2006) Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction, In Proceedings of 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS'06), IEEE Computer Society, pp. 708-713.

BibTeX: 
@inproceedings{PechenizkiyCBMS06,
  author = {Mykola Pechenizkiy and Aleksey Tsymbal and Seppo Puuronen and Oleksandr Pechenizkiy},
  title = {Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction},
  booktitle = {Proceedings of 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS'06)},
  publisher = {IEEE Computer Society},
  year = {2006},
  pages = {708--713},
  doi = {http://doi.org/10.1109/CBMS.2006.65},
  file = {PechenizkiyCBMS06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyCBMS06.pdf:PDF}
}
conference Tsymbal, A., Pechenizkiy, M. & Cunningham, P. (2006) Dynamic Integration with Random Forests, In Proceedings of 17th European Conference on Machine Learning (ECML/PKDD 2006), Lecture Notes in Computer Science, 4212, Springer, pp. 801-808.

BibTeX: 
@inproceedings{TsymbalECML06,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham},
  title = {Dynamic Integration with Random Forests},
  booktitle = {Proceedings of 17th European Conference on Machine Learning (ECML/PKDD 2006)},
  editor = {Johannes Fürnkranz and Tobias Scheffer and Myra Spiliopoulou},
  publisher = {Springer},
  year = {2006},
  volume = {4212},
  pages = {801--808},
  url = {http://dblp.uni-trier.de/db/conf/ecml/ecml2006.html#TsymbalPC06},
  doi = {http://doi.org/10.1007/11871842_82},
  file = {TsymbalECML06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalECML06.pdf:PDF}
}
technical report Tsymbal, A., Pechenizkiy, M. & Cunningham, P. (2006) Dynamic Integration with Random Forests Dept of Computer Science, Trinity College Dublin, Ireland, TCD-CS-2006-23.

BibTeX: 
@techreport{TsymbalTechRep06,
  author = {Aleksey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham},
  title = {Dynamic Integration with Random Forests},
  school = {Dept of Computer Science, Trinity College Dublin, Ireland},
  year = {2006},
  number = {TCD-CS-2006-23},
  file = {TCD-CS-2006-23.pdf:http//www.cs.tcd.ie/publications/tech-reports/reports.06/TCD-CS-2006-23.pdf:PDF}
}
conference Tsymbal, A., Pechenizkiy, M., Cunningham, P. & Puuronen, S. (2006) Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections., In Proceedings of 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS'06), IEEE Computer Society, pp. 679-684.

BibTeX: 
@inproceedings{TsymbalCBMS2006,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham and Seppo Puuronen},
  title = {Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections.},
  booktitle = {Proceedings of 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS'06)},
  publisher = {IEEE Computer Society},
  year = {2006},
  pages = {679--684},
  url = {http://dblp.uni-trier.de/db/conf/cbms/cbms2006.html#TsymbalPCP06},
  doi = {http://doi.org/10.1109/CBMS.2006.94},
  file = {TsymbalCBMS06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalCBMS06.pdf:PDF}
}
workshop, keynote Puuronen, S., Pechenizkiy, M. & Tsymbal, A. (2006) Keynote Paper: Data Mining Researcher, Who is Your Customer? Some Issues Inspired by the Information Systems Field, In Proc. 17th International Conference on Database and Expert Systems Applications (DEXA'06), IEEE Computer Society, pp. 579-583.

Abstract: Data mining as an applied research field is still causing great expectations among organizations which want to raise the utility they are getting from their huge databases and data warehouses. There exist too few success stories about organizations having managed to satisfy even some of those expectations. This situation is very similar to the one inside the information systems (IS) field, especially earlier but even currently. The recent lively debate about the identity of the IS discipline included also the analysis concerning the customers of IS research. Inspired by IS researchers' insights related to the topic, we ask the question "who is our customer?" as data mining researchers. With this we want to raise to discussion the border that limits the topics 'acceptable' to work with as a data mining researcher. We suggest in this paper that the border should be transferred more clearly towards the direction so that beside the technical concerns also at least some user- and organization-related research questions are included
BibTeX: 
@inproceedings{PuuronenDEXA06,
  author = {Puuronen, S. and Pechenizkiy, M. and Tsymbal, A.},
  title = {Keynote Paper: Data Mining Researcher, Who is Your Customer? Some Issues Inspired by the Information Systems Field},
  booktitle = {Proc. 17th International Conference on Database and Expert Systems Applications (DEXA'06)},
  publisher = {IEEE Computer Society},
  year = {2006},
  pages = {579--583},
  url = {http://dblp.uni-trier.de/db/conf/dexaw/dexaw2006.html#PuuronenPT06},
  doi = {http://doi.org/10.1109/DEXA.2006.81},
  file = {PuuronenDEXA06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PuuronenDEXA06.pdf:PDF}
}
journal Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2006) Local Dimensionality Reduction and Supervised Learning Within Natural Clusters for Biomedical Data Analysis, IEEE Transactions on Information Technology in Biomedicine, 10(3), pp. 533-539.

BibTeX: 
@article{PechenizkiyITBM06,
  author = {Pechenizkiy, M. and Tsymbal, A. and Puuronen, S.},
  title = {Local Dimensionality Reduction and Supervised Learning Within Natural Clusters for Biomedical Data Analysis},
  journal = {IEEE Transactions on Information Technology in Biomedicine},
  year = {2006},
  volume = {10},
  number = {3},
  pages = {533--539},
  doi = {http://doi.org/10.1109/TITB.2006.875654},
  file = {PechenizkiyITBM06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyITBM06.pdf:PDF}
}
journal Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2006) On Combining Principal Components with Parametric LDA-based Feature Extraction for Supervised Learning, Foundations of Computing and Decision Sciences, Special Issue ”Data Mining and Knowledge Discovery”, 31(1), pp. 59-73.

BibTeX: 
@article{PechenizkiyFCDS06,
  author = {Mykola Pechenizkiy and Aleksey Tsymbal and Seppo Puuronen},
  title = {On Combining Principal Components with Parametric LDA-based Feature Extraction for Supervised Learning},
  journal = {Foundations of Computing and Decision Sciences, Special Issue ”Data Mining and Knowledge Discovery”},
  year = {2006},
  volume = {31},
  number = {1},
  pages = {59--73},
  file = {PechenizkiyFCDS06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyFCDS06.pdf:PDF}
}
editorial Rennolls, K., Delisle, S. & Pechenizkiy, M. (2006) Preface to the Proceedings of 2nd International Workshop on Philosophies and Methodologies for Knowledge Discovery, Deployment, and Development of Decision Support Systems, In Proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA'06), IEEE Computer Society, pp. 577-578.

BibTeX: 
@inproceedings{RennollsPMKD06,
  author = {Keith Rennolls and Sylvain Delisle and Mykola Pechenizkiy},
  title = {Preface to the Proceedings of 2nd International Workshop on Philosophies and Methodologies for Knowledge Discovery, Deployment, and Development of Decision Support Systems},
  booktitle = {Proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA'06)},
  publisher = {IEEE Computer Society},
  year = {2006},
  pages = {577--578},
  file = {26410577.pdf:http//csdl2.computer.org/comp/proceedings/dexa/2006/2641/00/26410577.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M. & Puuronen, S. (2006) The Challenge of Feedback Personalization to Learning Styles in a Web-Based Learning System, In Proceedings of 6th International Conference on Advanced Learning Technologies (ICALT'06), IEEE Computer Society, pp. 1143-1144.

Abstract: Feedback is information that is provided to a user to inform him/her about the result of his/her action and to motivate him/her to further interact with the system. In Web-based learning systems (WBLS), feedback is particularly important in test and evaluation tasks. The main objective of the paper is twofold: (I) to encourage WBLS designers and specialists to pay more attention to the problem of feedback adaptation, and (2) to analyze suggestions for feedback personalization to learning styles in a WBLS
BibTeX: 
@inproceedings{VasilyevaICALT06,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and Seppo Puuronen},
  title = {The Challenge of Feedback Personalization to Learning Styles in a Web-Based Learning System},
  booktitle = {Proceedings of 6th International Conference on Advanced Learning Technologies (ICALT'06)},
  publisher = {IEEE Computer Society},
  year = {2006},
  pages = {1143--1144},
  url = {http://dblp.uni-trier.de/db/conf/icalt/icalt2006.html#VasilyevaPP06},
  doi = {http://doi.org/10.1109/ICALT.2006.1652664},
  file = {VasilyevaICALT06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaICALT06.pdf:PDF}
}
conference Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2006) The Impact of Sample Reduction on PCA-based Feature Extraction for Supervised Learning, In Proceedings of the ACM symposium on Applied computing (SAC'06), ACM Press, pp. 553-558.

Abstract: "The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Nave Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PCA and two eigenvector-based approaches that take into account class information. The first class-conditional approach is parametric and optimizes the ratio of between-class variance to the within-class variance of the transformed data. The second approach is a nonparametric modification of the first one based on the local calculation of the between-class covariance matrix. The experiments are conducted on ten UCI data sets, using four different strategies to select samples: (1) random sampling, (2) stratified random sampling, (3) kd-tree based selective sampling, and (4) stratified sampling with kd-tree based selection. Our experiments show that if the sample size for FE model construction is small then it is important to take into account both class information and data distribution. Further, for supervised learning the nonparametric FE approach needs much less instances to produce a new representation space that result in the same or higher classification accuracy than the other FE approaches.
BibTeX: 
@inproceedings{PechenizkiySAC06,
  author = {Pechenizkiy, Mykola and Puuronen, Seppo and Tsymbal, Alexey},
  title = {The Impact of Sample Reduction on PCA-based Feature Extraction for Supervised Learning},
  booktitle = {Proceedings of the ACM symposium on Applied computing (SAC'06)},
  publisher = {ACM Press},
  year = {2006},
  pages = {553--558},
  url = {http://dblp.uni-trier.de/db/conf/sac/sac2006.html#PechenizkiyPT06},
  doi = {http://doi.org/10.1145/1141277.1141406},
  file = {PechenizkiySAC06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiySAC06.pdf:PDF}
}
workshop Pechenizkiy, M., Tourunen, A., Kärkkäinen, T., Ivannikov, A. & Nevalainen, H. (2006) Towards Better Understanding of Circulating Fluidized Bed Boilers: a Data Mining Approach, In Proceedings ECML/PKDD Workshop on Practical Data Mining: Applications, Experiences and Challenges (DMBiz'06), pp. 80-83.

BibTeX: 
@inproceedings{PechenizkiyECML06,
  author = {Mykola Pechenizkiy and Antti Tourunen and Tommi Kärkkäinen and Andriy Ivannikov and H. Nevalainen},
  title = {Towards Better Understanding of Circulating Fluidized Bed Boilers: a Data Mining Approach},
  booktitle = {Proceedings ECML/PKDD Workshop on Practical Data Mining: Applications, Experiences and Challenges (DMBiz'06)},
  year = {2006},
  pages = {80-83},
  file = {PechenizkiyECML06.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyECML06.pdf:PDF}
}
workshop Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2005) Competitive Advantage from Data Mining: Some Lessons Learnt in the Information Systems Field, In Proceedings of 16th International Workshop on Database and Expert Systems Applications (DEXA'05 Workshops), IEEE Computer Society, pp. 733-737.

BibTeX: 
@inproceedings{PechenizkiyDEXA05a,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Alexey Tsymbal},
  title = {Competitive Advantage from Data Mining: Some Lessons Learnt in the Information Systems Field},
  booktitle = {Proceedings of 16th International Workshop on Database and Expert Systems Applications (DEXA'05 Workshops)},
  publisher = {IEEE Computer Society},
  year = {2005},
  pages = {733--737},
  doi = {http://doi.org/10.1109/DEXA.2005.64},
  file = {PechenizkiyDEXA05a.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyDEXA05a.pdf:PDF}
}
conference Pechenizkiy, M. (2005) Data Mining Strategy Selection via Empirical and Constructive Induction, In Proceedings of the IASTED International Conference Databases and Applications (DBA'05), IASTED/ACTA Press, pp. 59-64.

BibTeX: 
@inproceedings{PechenizkiyDBA05,
  author = {Mykola Pechenizkiy},
  title = {Data Mining Strategy Selection via Empirical and Constructive Induction},
  booktitle = {Proceedings of the IASTED International Conference Databases and Applications (DBA'05)},
  editor = {M. H. Hamza},
  publisher = {IASTED/ACTA Press},
  year = {2005},
  pages = {59--64},
  file = {PechenizkiyDBA05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyDBA05.pdf:PDF}
}
journal Tsymbal, A., Pechenizkiy, M. & Cunningham, P. (2005) Diversity in Search Strategies for Ensemble Feature Selection., Information Fusion, 6(1), pp. 83-98.

BibTeX: 
@article{TsymbalIF05,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham},
  title = {Diversity in Search Strategies for Ensemble Feature Selection.},
  journal = {Information Fusion},
  year = {2005},
  volume = {6},
  number = {1},
  pages = {83--98},
  url = {http://dblp.uni-trier.de/db/journals/inffus/inffus6.html#TsymbalPC05},
  doi = {http://doi.org/10.1016/j.inffus.2004.04.003},
  file = {TsymbalIF05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalIF05.pdf:PDF}
}
thesis Pechenizkiy, M. (2005) Feature Extraction for Supervised Learning in Knowledge Discovery Systems PhD Thesis, University of Jyväskylä, Finland.

BibTeX: 
@techreport{PechenizkiyPHD05,
  author = {Mykola Pechenizkiy},
  title = {Feature Extraction for Supervised Learning in Knowledge Discovery Systems},
  school = {PhD Thesis, University of Jyväskylä, Finland},
  year = {2005},
  file = {PechenizkiyPHD05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyPHD05.pdf:PDF}
}
workshop Vasilyeva, E., Pechenizkiy, M. & Puuronen, S. (2005) Feedback Adaptation in Web-based Applications, In Proceedings of International Workshop on Combining Intelligent and Adaptive Hypermedia Methods/Techniques in Web-Based Education Systems (CIAH'05) at ACM Hypertext 2005 Conference, pp. 85-90.

BibTeX: 
@inproceedings{VasilyevaCIAH05,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and Seppo Puuronen},
  title = {Feedback Adaptation in Web-based Applications},
  booktitle = {Proceedings of International Workshop on Combining Intelligent and Adaptive Hypermedia Methods/Techniques in Web-Based Education Systems (CIAH'05) at ACM Hypertext 2005 Conference},
  year = {2005},
  pages = {85--90},
  file = {VasilyevaCIAH05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaCIAH05.pdf:PDF}
}
conference Pechenizkiy, M., Tsymbal, A., Puuronen, S., Shifrin, M. & Alexandrova, I. (2005) Knowledge Discovery from Microbiology Data: Many-sided Analysis of Antibiotic Resistance in Nosocomial Infections, In Post-Conference Proceedings of 3rd Conference on Professional Knowledge Management: Experiences and Visions, Lecture Notes in Computer Science, 3782, Springer Berlin / Heidelberg, pp. 360-372.

BibTeX: 
@inproceedings{PechenizkiyPKM05,
  author = {Mykola Pechenizkiy and Aleksey Tsymbal and Seppo Puuronen and Michail Shifrin and Irina Alexandrova},
  title = {Knowledge Discovery from Microbiology Data: Many-sided Analysis of Antibiotic Resistance in Nosocomial Infections},
  booktitle = {Post-Conference Proceedings of 3rd Conference on Professional Knowledge Management: Experiences and Visions},
  editor = {K.D. Althoff et al.},
  publisher = {Springer Berlin / Heidelberg},
  year = {2005},
  volume = {3782},
  pages = {360--372},
  doi = {http://doi.org/10.1007/11590019_41},
  file = {PechenizkiyPKM05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyPKM05.pdf:PDF}
}
workshop Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2005) Knowledge Management Challenges in Knowledge Discovery Systems, In Proceedings of 16th International Workshop on Database and Expert Systems Applications (DEXA'05 Workshops), IEEE Computer Society, pp. 433-437.

Abstract: Current knowledge discovery systems are armed with many data mining techniques that can be potentially applied to a new problem. However, a system faces a challenge of selecting the most appropriate technique(s) for a problem at hand, since in the real domain area it is infeasible to perform a comparison of all applicable techniques. The main goal of this paper is to consider the limitations of data-driven approaches and propose a knowledge-driven approach to enhance the use of multiple data-mining strategies in a knowledge discovery system. We introduce the concept of (meta-) knowledge management, which is aimed to organize a systematic process of (meta-) knowledge capture and refinement over time
BibTeX: 
@inproceedings{PechenizkiyDEXA05b,
  author = {Mykola Pechenizkiy and Aleksey Tsymbal and Seppo Puuronen},
  title = {Knowledge Management Challenges in Knowledge Discovery Systems},
  booktitle = {Proceedings of 16th International Workshop on Database and Expert Systems Applications (DEXA'05 Workshops)},
  publisher = {IEEE Computer Society},
  year = {2005},
  pages = {433--437},
  url = {http://dblp.uni-trier.de/db/conf/dexaw/dexaw2005.html#PechenizkiyTP05},
  doi = {http://doi.org/10.1109/DEXA.2005.124},
  file = {PechenizkiyDEXA05b.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyDEXA05b.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M. & Puuronen, S. (2005) Knowledge Management Challenges in Web-Based Adaptive e-Learning Systems, In Proceedings of 5th International Conference on Knowledge Management (I-KNOW'05), J.UCS with Springer, pp. 112-119.

BibTeX: 
@inproceedings{VasilyevaIKNOW05,
  author = {Ekaterina Vasilyeva and Mykola Pechenizkiy and Seppo Puuronen},
  title = {Knowledge Management Challenges in Web-Based Adaptive e-Learning Systems},
  booktitle = {Proceedings of 5th International Conference on Knowledge Management (I-KNOW'05)},
  publisher = {J.UCS with Springer},
  year = {2005},
  pages = {112--119},
  url = {http://i-know.tugraz.at/blog/2005/11/knowledge-management-challenges-in-web-based-adaptive-e-learning-systems},
  file = {VasilyevaIKNOW05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaIKNOW05.pdf:PDF}
}
conference Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2005) Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis, In Proceedings of 18th IEEE Symposium on Computer-Based Medical Systems (CBMS '05), IEEE Computer Society, pp. 365-370.

Abstract: Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before applying a learning algorithm. Especially it is important for multidimensional heterogeneous data, presented by a large number of features of different types. Dimensionality reduction is one commonly applied approach. The goal of this paper is to study the impact of natural clustering on dimensionality reduction for classification. We compare several data mining strategies that apply dimensionality reduction by means of feature extraction or feature selection for subsequent classification. We show experimentally on microbiological data that local dimensionality reduction within natural clusters results in a better feature space for classification in comparison with the global search in terms of generalization accuracy.
BibTeX: 
@inproceedings{PechenizkiyCBMS05,
  author = {Pechenizkiy, M. and Tsymbal, A. and Puuronen, S.},
  title = {Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis},
  booktitle = {Proceedings of 18th IEEE Symposium on Computer-Based Medical Systems (CBMS '05)},
  publisher = {IEEE Computer Society},
  year = {2005},
  pages = {365--370},
  doi = {http://doi.org/10.1109/CBMS.2005.71},
  file = {PechenizkiyCBMS05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyCBMS05.pdf:PDF}
}
workshop Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2005) On Combining Principal Components with Parametric LDA-based Feature Extraction for Supervised Learning, In Proceedings of 1st International Workshop on Data Mining and Knowledge Discovery (ADMKD'05), pp. 47-56.

BibTeX: 
@inproceedings{PechenizkiyADMKD05,
  author = {Mykola Pechenizkiy and Aleksey Tsymbal and Seppo Puuronen},
  title = {On Combining Principal Components with Parametric LDA-based Feature Extraction for Supervised Learning},
  booktitle = {Proceedings of 1st International Workshop on Data Mining and Knowledge Discovery (ADMKD'05)},
  editor = {T.Morzy et al.},
  year = {2005},
  pages = {47--56},
  url = {http://www.cs.ioc.ee/adbis2005/index.php?page=26},
  file = {PechenizkiyADMKD05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyADMKD05.pdf:PDF}
}
conference Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2005) On the Use of Information Systems Research Methods in Data Mining, In International Conference on Information Systems Development: Advances in Theory, Practice and Education (ISD'04), Springer US, pp. 487-499.

BibTeX: 
@inproceedings{PechenizkiyISD05,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Aleksey Tsymbal},
  title = {On the Use of Information Systems Research Methods in Data Mining},
  booktitle = {International Conference on Information Systems Development: Advances in Theory, Practice and Education (ISD'04)},
  editor = {Olegas Vasilecas and Wita Wojtkowski and Jože Zupančič and Albertas Caplinskas and W. Gregory Wojtkowski and Stanisław Wrycza},
  publisher = {Springer US},
  year = {2005},
  pages = {487--499},
  doi = {http://doi.org/10.1007/0-387-28809-0_42},
  file = {PechenizkiyISD05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyISD05.pdf:PDF}
}
conference Tsymbal, A., Pechenizkiy, M. & Cunningham, P. (2005) Sequential Genetic Search for Ensemble Feature Selection, In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI'05), Professional Book Center, pp. 877-882.

BibTeX: 
@inproceedings{TsymbalIJCAI05,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham},
  title = {Sequential Genetic Search for Ensemble Feature Selection},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI'05)},
  editor = {Leslie Pack Kaelbling and Alessandro Saffiotti},
  publisher = {Professional Book Center},
  year = {2005},
  pages = {877--882},
  url = {http://www.ijcai.org/papers/1312.pdf},
  file = {TsymbalIJCAI05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalIJCAI05.pdf:PDF}
}
conference Pechenizkiy, M. (2005) The Impact of Feature Extraction on the Performance of a Classifier: kNN, Naïve Bayes and C4.5., In Canadian Conference on AI, Lecture Notes in Computer Science, 3501, Springer, pp. 268-279.

BibTeX: 
@inproceedings{PechenizkiyCanAI05,
  author = {Mykola Pechenizkiy},
  title = {The Impact of Feature Extraction on the Performance of a Classifier: kNN, Naïve Bayes and C4.5.},
  booktitle = {Canadian Conference on AI},
  editor = {Balázs Kégl and Guy Lapalme},
  publisher = {Springer},
  year = {2005},
  volume = {3501},
  pages = {268--279},
  doi = {http://doi.org/10.1007/11424918_28},
  file = {PechenizkiyCanAI05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyCanAI05.pdf:PDF}
}
conference Vasilyeva, E., Pechenizkiy, M. & Puuronen, S. (2005) Towards the Framework of Adaptive User Interfaces for eHealth, In Proceedings of 18th IEEE Symposium on Computer-Based Medical Systems (CBMS '05), IEEE Computer Society, pp. 139-144.

BibTeX: 
@inproceedings{VasilyevaCBMS05,
  author = {Vasilyeva, E. and Pechenizkiy, M. and Puuronen, S.},
  title = {Towards the Framework of Adaptive User Interfaces for eHealth},
  booktitle = {Proceedings of 18th IEEE Symposium on Computer-Based Medical Systems (CBMS '05)},
  publisher = {IEEE Computer Society},
  year = {2005},
  pages = {139--144},
  doi = {http://doi.org/10.1109/CBMS.2005.101},
  file = {VasilyevaCBMS05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/VasilyevaCBMS05.pdf:PDF}
}
workshop Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2005) Why Data Mining Does Not Contribute to Business?, In Proceedings of Data Mining for Business Workshop (DMBiz'05) at ECML/PKDD 2005 Conference, pp. 67-71.

BibTeX: 
@inproceedings{PechenizkiyDMBIZ05,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Aleksey Tsymbal},
  title = {Why Data Mining Does Not Contribute to Business?},
  booktitle = {Proceedings of Data Mining for Business Workshop (DMBiz'05) at ECML/PKDD 2005 Conference},
  editor = {C. Soares et al.},
  year = {2005},
  pages = {67--71},
  file = {PechenizkiyDMBIZ05.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyDMBIZ05.pdf:PDF}
}
conference Tsymbal, A., Pechenizkiy, M. & Cunningham, P. (2004) Diversity in Random Subspacing Ensembles, In Proceedings of 6th International Conference on Data Warehousing and Knowledge Discovery (DaWaK'04), Lecture Notes in Computer Science, 3181, Springer Verlag, pp. 309-319.

BibTeX: 
@inproceedings{TsymbalDaWaK04,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Padraig Cunningham},
  title = {Diversity in Random Subspacing Ensembles},
  booktitle = {Proceedings of 6th International Conference on Data Warehousing and Knowledge Discovery (DaWaK'04)},
  editor = {Y. Kambayashi and M. Mohania and W. Wöß},
  publisher = {Springer Verlag},
  year = {2004},
  volume = {3181},
  pages = {309-319},
  doi = {http://www.springerlink.com/content/lqahnnly5rmb47wy},
  file = {TsymbalDaWak04.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalDaWak04.pdf:PDF}
}
thesis Pechenizkiy, M. (2004) Feature Extraction for Classification: Issues of Intelligent Integration Licentiate Thesis, University of Jyväskylä, Finland.

BibTeX: 
@techreport{PecehnizkiyLic04,
  author = {Mykola Pechenizkiy},
  title = {Feature Extraction for Classification: Issues of Intelligent Integration},
  school = {Licentiate Thesis. University of Jyväskylä, Finland},
  year = {2004},
  file = {PechenizkiyLic04.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyLic04.pdf:PDF}
}
conference Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2004) Meta-Knowledge Management in Multistrategy Process-Oriented Knowledge Discovery Systems, In Proceedings of 13th International Conference on Information Systems Development. Advances in Theory, Practice and Education (ISD'04), Technika, pp. 317-328.

BibTeX: 
@inproceedings{PechenizkiyISD04,
  author = {Mykola Pechenizkiy and Alexey Tsymbal and Seppo Puuronen},
  title = {Meta-Knowledge Management in Multistrategy Process-Oriented Knowledge Discovery Systems},
  booktitle = {Proceedings of 13th International Conference on Information Systems Development. Advances in Theory, Practice and Education (ISD'04)},
  editor = {O. Vasilekas et al.},
  publisher = {Technika},
  year = {2004},
  pages = {317--328}
}
conference Pechenizkiy, M., Tsymbal, A. & Puuronen, S. (2004) PCA-based Feature Transformation for Classification: Issues in Medical Diagnostics, In Proceedings of 7th IEEE International Symposium on Computer-Based Medical Systems (CBMS'04), IEEE Computer Society, pp. 535-540.

Abstract: The goal of this paper is to propose, evaluate, and compare several data mining strategies that apply feature transformation for subsequent classification, and to consider their application to medical diagnostics. We (1) briefly consider the necessity of dimensionality reduction and discuss why feature transformation may work better than feature selection for some problems; (2) analyze experimentally whether extraction of new components and replacement of original features by them is better than storing the original features as well; (3) consider how important the use of class information is in the feature extraction process; and (4) discuss some interpretability issues regarding the extracted features.
BibTeX: 
@inproceedings{PechenizkiyCBMS04,
  author = {Mykola Pechenizkiy and Alexey Tsymbal and Seppo Puuronen},
  title = {PCA-based Feature Transformation for Classification: Issues in Medical Diagnostics},
  booktitle = {Proceedings of 7th IEEE International Symposium on Computer-Based Medical Systems (CBMS'04)},
  editor = {R. Long et al.},
  publisher = {IEEE Computer Society},
  year = {2004},
  pages = {535--540},
  doi = {http://doi.org/10.1109/CBMS.2004.1311770},
  file = {PechenizkiyCBMS04.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyCBMS04.pdf:PDF}
}
workshop Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2004) The Iterative and Interactive Data Mining Process: the Information Systems Development and Knowledge Management Perspectives, In Proceedings of Workshop on Foundations of Data Mining at 4th International IEEE ICDM'04 Conference, pp. 129-136.

BibTeX: 
@inproceedings{PechenizkiyICDM04,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Alexey Tsymbal},
  title = {The Iterative and Interactive Data Mining Process: the Information Systems Development and Knowledge Management Perspectives},
  booktitle = {Proceedings of Workshop on Foundations of Data Mining at 4th International IEEE ICDM'04 Conference},
  editor = {T.Y. Lin and S. Smale and T. Poggio and C.J. Liau},
  year = {2004},
  pages = {129--136},
  file = {PechenizkiyICDM04.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyICDM04.pdf:PDF}
}
conference Tsymbal, A., Pechenizkiy, M., Puuronen, S. & Patterson, D.W. (2003) Dynamic Integration of Classifiers in the Space of Principal Components, In Proceedings of Advances in Databases and Information Systems: 7th East-European Conference (ADBIS'03), Lecture Notes in Computer Science, 2798, Springer-Verlag, pp. 278-292.

BibTeX: 
@inproceedings{TsymbalADBIS03,
  author = {Alexey Tsymbal and Mykola Pechenizkiy and Seppo Puuronen and David W. Patterson},
  title = {Dynamic Integration of Classifiers in the Space of Principal Components},
  booktitle = {Proceedings of Advances in Databases and Information Systems: 7th East-European Conference (ADBIS'03)},
  editor = {Leonid A. Kalinichenko and Rainer Manthey and Bernhard Thalheim and Uwe Wloka},
  publisher = {Springer-Verlag},
  year = {2003},
  volume = {2798},
  pages = {278--292},
  doi = {http://www.springerlink.com/content/lyggd7ue5vyc0l2b},
  file = {TsymbalADBIS03.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalADBIS03.pdf:PDF}
}
conference Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2003) Feature Extraction for Classification in Knowledge Discovery Systems, In Proceedings of 7th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES'03), Lecture Notes in Computer Science, 2773, Springer, pp. 526-532.

BibTeX: 
@inproceedings{PechenizkiyKES03,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Alexey Tsymbal},
  title = {Feature Extraction for Classification in Knowledge Discovery Systems},
  booktitle = {Proceedings of 7th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES'03)},
  editor = {Vasile Palade and Robert J. Howlett and Lakhmi C. Jain},
  publisher = {Springer},
  year = {2003},
  volume = {2773},
  pages = {526--532},
  doi = {http://www.springerlink.com/content/kf680cr4ul58tch3},
  file = {PechenizkiyKES03.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/PechenizkiyKES03.pdf:PDF}
}
journal Pechenizkiy, M., Puuronen, S. & Tsymbal, A. (2003) Feature Extraction for Classification in the Data Mining Process, International Journal on Information Theories and Applications, 10(1), pp. 271-278.

BibTeX: 
@article{PechenizkiyITA03,
  author = {Mykola Pechenizkiy and Seppo Puuronen and Alexey Tsymbal},
  title = {Feature Extraction for Classification in the Data Mining Process},
  journal = {International Journal on Information Theories and Applications},
  year = {2003},
  volume = {10},
  number = {1},
  pages = {271--278},
  url = {http://www.foibg.com/ijita/vol10/ijita-fv10.htm},
  file = {ijita10-3-p06.pdf:http//www.foibg.com/ijita/vol10/ijita10-3-p06.pdf:PDF}
}
conference Tsymbal, A., Cunningham, P., Pechenizkiy, M. & Puuronen, S. (2003) Search Strategies for Ensemble Feature Selection in Medical Diagnostics, In Proceedings of 16th IEEE Symposium Computer-Based Medical Systems (CBMS'03), IEEE Computer Society, pp. 124-129.

Abstract: The goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal pain. Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to get higher accuracy, sensitivity, and specificity, which are often not achievable with single models. One technique, which proved to be effective for ensemble construction, is feature selection. Lately, several strategies for ensemble feature selection were proposed, including random subspacing, hill-climbing-based search, and genetic search. In this paper, we propose two new sequential-search-based strategies for ensemble feature selection, and evaluate them, constructing ensembles of simple Bayesian classifiers for the problem of acute abdominal pain classification. We compare the search strategies with regard to achieved accuracy, sensitivity, specificity, and the average number of features they select.
BibTeX: 
@inproceedings{TsymbalCBMS03,
  author = {Alexey Tsymbal and Padraig Cunningham and Mykola Pechenizkiy and Seppo Puuronen},
  title = {Search Strategies for Ensemble Feature Selection in Medical Diagnostics},
  booktitle = {Proceedings of 16th IEEE Symposium Computer-Based Medical Systems (CBMS'03)},
  publisher = {IEEE Computer Society},
  year = {2003},
  pages = {124-129},
  doi = {http://doi.org/10.1109/CBMS.2003.1212777},
  file = {TsymbalCBMS03.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalCBMS03.pdf:PDF}
}
conference Puuronen, S., Patterson, D., Tsymbal, A. & Pechenizkiy, M. (2002) Dimensionality Reduction for Classification Tasks Modelling, In Proceedings of 43rd Scandinavian Conference on Simulation and Modelling (SIMS'02), pp. 35-44.

BibTeX: 
@inproceedings{PuuronenSIMS02,
  author = {Seppo Puuronen and Dave Patterson and Alexey Tsymbal and Mykola Pechenizkiy},
  title = {Dimensionality Reduction for Classification Tasks Modelling},
  booktitle = {Proceedings of 43rd Scandinavian Conference on Simulation and Modelling (SIMS'02)},
  year = {2002},
  pages = {35--44},
  url = {http://ntsat.oulu.fi/Tapahtumat/SIMS_CallForPapers/default.htm}
}
conference Tsymbal, A., Puuronen, S., Pechenizkiy, M., Baumgarten, M. & Patterson, D.W. (2002) Eigenvector-Based Feature Extraction for Classification, In Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS'02), AAAI Press, pp. 354-358.

BibTeX: 
@inproceedings{TsymbalFLAIRS02,
  author = {Alexey Tsymbal and Seppo Puuronen and Mykola Pechenizkiy and Matthias Baumgarten and David W. Patterson},
  title = {Eigenvector-Based Feature Extraction for Classification},
  booktitle = {Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS'02)},
  editor = {Susan M. Haller and Gene Simmons},
  publisher = {AAAI Press},
  year = {2002},
  pages = {354--358},
  doi = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.109.3981\&rep=rep1\&type=pdf},
  file = {TsymbalFLAIRS02.pdf:http//www.win.tue.nl/ mpechen/publications/pubs/TsymbalFLAIRS02.pdf:PDF}
}

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