Core-generating discretization for rough set feature selection

  • Authors:

  • David Tian;Xiao-Jun Zeng;John Keane

  • Affiliations:

  • Department of Computing, Faculty of ACES, Sheffield Hallam University, Sheffield, UK;School of Computer Science, University of Manchester, Manchester, UK;School of Computer Science, University of Manchester, Manchester, UK

  • Venue:
  • Transactions on rough sets XIII
  • Year:
  • 2011

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Abstract

Rough set feature selection (RSFS) can be used to improve classifier performance. RSFS removes redundant attributes whilst keeping important ones that preserve the classification power of the original dataset. The feature subsets selected by RSFS are called reducts. The intersection of all reducts is called core. However, RSFS handles discrete attributes only. To process datasets consisting of real attributes, they are discretized before applying RSFS. Discretization controls core of the discrete dataset. Moreover, core may critically affect the classification performance of reducts. This paper defines core-generating discretization, a type of discretization method; analyzes the properties of core-generating discretization; models core-generating discretization using constraint satisfaction; defines core-generating approximate minimum entropy (C-GAME) discretization; models C-GAME using constraint satisfaction and evaluates the performance of C-GAME as a pre-processor of RSFS using ten datasets from the UCI Machine Learning Repository.