Discretization of Continuous Attributes for Learning Classification Rules

  • Aijun An
  • Nick Cercone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1574)

Abstract

We present a comparison of three entropy-based discretization methods in a context of learning classification rules. We compare the binary recursive discretization with a stopping criterion based on the Minimum Description Length Principle (MDLP)[3], a non-recursive method which simply chooses a number of cut-points with the highest entropy gains, and a non-recursive method that selects cut-points according to both information entropy and distribution of potential cut-points over the instance space. Our empirical results show that the third method gives the best predictive performance among the three methods tested.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Aijun An
    • 1
  • Nick Cercone
    • 1
  1. 1.Department of Computer ScienceUniversity of WaterlooWaterlooCanada

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