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Evolving Fuzzy Decision Trees with Genetic Programming and Clustering

  • Jeroen Eggermont
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)

Abstract

In this paper we present a new fuzzy decision tree representation for data classification using genetic programming. The new fuzzy representation utilizes fuzzy clusters for handling continuous attributes. To make optimal use of the fuzzy classifications of this representation an extra fitness measure is used. The new fuzzy representation will be compared, using several machine learning data sets, to a similar non-fuzzy representation as well as to some other evolutionary and non-evolutionary algorithms from literature.

Keywords

Membership Function Genetic Program Fuzzy Membership Function Fuzzy Representation Fuzzy Association Rule 
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 2002

Authors and Affiliations

  • Jeroen Eggermont
    • 1
  1. 1.Leiden Institute of Advanced Computer ScienceUniversiteit LeidenThe Netherlands

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