Rule-Based Estimation of Attribute Relevance

  • Jerzy Błaszczyński
  • Roman Słowiński
  • Robert Susmaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6954)

Abstract

We consider estimation of relevance of attributes used for classification. This estimation takes into account the predictive capabilities of the attributes. To this end, we are using Bayesian confirmation measure. The estimation is based on analysis of rule classifiers in classification tests. The attribute relevance measure increases when more rules involving this attribute suggest a correct decision, or when more rules that do not involve this attribute suggest an incorrect decision in the classification test; otherwise, the attribute relevance measure is decreasing. This requirement is satisfied by a monotonic Bayesian confirmation measure. Usefulness of the presented measure is verified experimentally.

Keywords

attribute relevance Bayesian confirmation decision rule classification ensemble classifier 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jerzy Błaszczyński
    • 1
  • Roman Słowiński
    • 1
    • 2
  • Robert Susmaga
    • 1
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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