New Plug-in: DrFurby Classifier

Input

  1. Training log (XLog)
  2. Test log (XLog)

Output

  1. Test log (XLog) with classified traces (using DrFurby Extension, see below).

Package

  • DivideAndConquer

DrFurby Classifier Plug-in

The DrFurby Classifier plug-in takes a training log and a test log, and returns a copy of the test log where every trace in the test log is classified as fitting (positive) or non-fitting (negative) to the training log. The main idea behind this plug-in is that weuse different techniques to avoid false positives and false negatives as much as possible:

  • False negatives are avoided as much as possible by only using techniques that guarantee perfect fitness. As a result, none of the traces from the training log will be classified as negative.
  • False positives are avoided as much as possible by using a knock-out system among these techniques. If one technique considers the trace to be negative, it will be negative.

At the moment, the following techniques are used:

  • The Inductive Miner – infrequent with the noise setting set to 0.0 (which guarantees perfect fitness), combined with maximal decomposition (called IM-100).
  • The Hybrid ILP Miner, combined with no decomposition (called HIM-0).

If too many false positives remain, then other perfect-fitness guaranteeing techniques (ILP Miner, non-maximal decomposition, …) could be added to the plug-in, leading to additional classifications per trace. So far, it seems to work okay, as adding these other techniques seems not to improve the classification.

DrFurby Extension

A specific DrFurby Extension is used to enrich the copy of the test log with classification data. This extension provides the following attributes:

Attribute Level Type Description
classification trace String Classification of the trace (“positive” or “negative”)
him0Costs trace Continuous The costs of replaying this trace on the net as discovered by HIM-0
im100Costs trace Continuous The costs of replaying this trace on the net as discovered by IM-100.
millis log Discrete The number of milliseconds to it took classify the log.
name log Literal The name of the classified log.
negative log Discrete The number of traces classified negative.
positive log Discrete The number of traces classified positive.
totalCosts trace Continuous The accumulated costs of replaying this trace on all discovered nets.

DrFurby Submission

Our “DrFurby” submission to the contest can be found here.

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