Applications of Approximate Reducts to the Feature Selection Problem

  • Andrzej Janusz
  • Sebastian Stawicki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6954)

Abstract

In this paper we overview two feature rankings methods that utilize basic notions from the rough set theory, such as the idea of the decision reducts. We also propose a new algorithm, called Rough Attribute Ranker. In our approach, the usefulness of features is measured by their impact on quality of the reducts that contain them. We experimentally compare the reduct-based methods with several classic attribute rankers using synthetic, as well as real-life high dimensional datasets.

Keywords

attribute filtering feature selection approximate reducts 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrzej Janusz
    • 1
  • Sebastian Stawicki
    • 1
  1. 1.Faculty of Mathematics, Informatics, and MechanicsThe University of WarsawWarszawaPoland

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