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Evaluation Measures for Multi-class Subgroup Discovery

  • Tarek Abudawood
  • Peter Flach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)

Abstract

Subgroup discovery aims at finding subsets of a population whose class distribution is significantly different from the overall distribution. It has previously predominantly been investigated in a two-class context. This paper investigates multi-class subgroup discovery methods. We consider six evaluation measures for multi-class subgroups, four of them new, and study their theoretical properties. We extend the two-class subgroup discovery algorithm CN2-SD to incorporate the new evaluation measures and a new weighting scheme inspired by AdaBoost. We demonstrate the usefulness of multi-class subgroup discovery experimentally, using discovered subgroups as features for a decision tree learner. Not only is the number of leaves of the decision tree reduced with a factor between 8 and 16 on average, but significant improvements in accuracy and AUC are achieved with particular evaluation measures and settings. Similar performance improvements can be observed when using naive Bayes.

Keywords

Mutual Information Evaluation Measure Gini Index Target Concept Heuristic Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tarek Abudawood
    • 1
  • Peter Flach
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolUnited Kingdom

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