|Project Title:||Handling Concept Drift in Adaptive Information Systems|
In data mining field, the problem of concept drift has been recognized and
actively studied almost for two decades. It refers to changes in the concepts
underlying the data, or in the distribution of the data over time. These
changes effect the performance of models inferred from the historical data,
some of which may be no longer relevant. This project investigates the problem of
concept drift from an application perspective. We consider the application areas where the problem of concept drift is relevant. The
goal is to provide a reference framework presenting a whole spectrum of problems related to real applications in which handling of concept drift is
During the executing of this project we have performed several case studies, including food wholesales, laboratory-scale industrial boiler operation and stress detection from sensor data.
The project was funding PhD research of Jorn Bakker, who successfully defended his thesis on Dec 10th, 2012.
However, we continue studying how to detect and handle different kinds of changes or drifts in evolving data, including sensor streams (see PhD research of Alexandr Maslov), click-streams (see PhD research of Julia Kiseleva) and business process event logs (see research of JC Bose).
Visitors and Collaboration
- Dr. Indrė Žliobaitė (2008-2012)
- Dr. Albert Bifet, Waikato University, New Zealand, August 2010;
- Dr. Oleksiy Mazhelis, University of Jyvaskyla, Finland, August 2010;
- Dr. Carlos Soares, University of Porto, Portugal, November 2009.
- Advanced Topics in Data Stream Mining. ECMLPKDD 2012 tutorial, Bristol, UK (September 2012)
- Handling Concept Drift: Importance, Challenges and Solutions. Tutorial at the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011), Shenzhen, China (May 2011)
- Handling Concept Drift in Adaptive Information Systems (HaCDAIS 2011) Workshop at IEEE ICDM 2011;
- Dealing with Concept Drift in Adaptive Information Systems. “Learning from Evolving Data” tutorial block at ECML/PKDD 2010, Barcelona, Spain, (September 2010)
- Handling Concept Drift in Medical Applications: Importance, Challenges and Solution, Tutorial at IEEE Symposium on Computer Based Medical Systems (CBMS 2010), Perth, Australia, October 2010;
- Handling Concept Drift in AIS: Importance, Challenges and Solutions (HaCDAIS 2010) Workshop at ECML/PKDD'2010, Barcelona, Spain, September 2010;
- DH Group workshop on Detecting and Handling Drift (DHDHD 2010), Eindhoven, August 2010.
- Pechenizkiy, M. and ˇliobaite, I. (2012) Introduction to the special issue on handling concept drift in adaptive information systems, Evolving Systems, Springer-Verlag, pp. 1-2.
- Ang, H., Gopalkrishnan, V., ˇliobaite, I., Pechenizkiy, M., Hoi, S. (2012) Predictive Handling of Asynchronous Concept Drifts in Distributed Environments, IEEE Transactions on Knowledge and Data Engineering
- ˇliobaite, I., Bakker, J. and Pechenizkiy, M. (2012) Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?, Expert Systems With Applications, 39(1), pp. 806-815.
- Bakker, J., Pechenizkiy, M. and 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.
- Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009). OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers. Discovery Science 2009: 272-286. [PDF] [BIB]
- Pechenizkiy, M., Bakker, J., ˇliobaite, 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.
- Andriy Ivannikov, Mykola Pechenizkiy, Jorn Bakker, Timo Leino, Mikko Jegoroff, Tommi Kärkkäinen, Sami Äyrämö. 2009. Online Mass Flow Prediction in CFB Boilers. ICDM 2009: 206-219. [PDF] [BIB]
- Jorn Bakker, Mykola Pechenizkiy, Indre Zliobaite, Andriy Ivannikov, Tommi Kärkkäinen. 2009. Handling outliers and concept drift in online mass flow prediction in CFB boilers. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 13-22 (Best paper award)
Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009). Towards context aware food sales prediction. In Y. Saygin et al. (Eds.), Proceedings 3nd International Workshop on Domain Driven Data Mining (DDDM'09), in: IEEE International Conference on Data Mining: Workshops (ICDM'09, Miami, Florida, USA, December 6-9, 2009). (pp. 94-99). [PDF] [BIB]
Bakker, J., Pechenizkiy, M. (2009). Food wholesales prediction : what is your baseline? In J. Rauch, Z.W. Ras, P. Berka, T. Elomaa (Eds.), Foundations of Intelligent Systems (18th International Symposium, ISMIS 2009, Prague, Czech Republic, September 14-17, 2009. Proceedings). (Lecture Notes in Computer Science, Vol. 5722, pp. 493-502). Berlin: Springer. [PDF] [BIB]
Meulstee, P., Pechenizkiy, M. (2008). Food sales prediction: "If only it knew what we know". Proceedings 2nd International Workshop on Domain Driven Data Mining (DDDM'08), in: IEEE International Conference on Data Mining: Workshops (ICDM'08, Pisa, Italy, December 15-19, 2008). (pp. 134-143). IEEE Computer Society. [PDF] [BIB]
Unpublished Technical Reports
- ˇliobaite, I. and Pechenizkiy, M. (2010) Reference Framework for Handling Concept Drift: An Application Perspective. Eindhoven University of Technology.
Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2010). Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? (SIGKDD2010 Submission, Industry track).
- Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2010). Context Aware Sales Prediction: experimental evaluation. (TR-
Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009). Towards Context Aware Sales Prediction. (TR-Sep2009.pdf, exte
nded version of DDDM'09 submission).
Žliobaitė, I. (2009). On Recognition of Seasonal Predictability in Food Product Sales. (TR-Jul2009.pdf).
Presentations and posters