GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

  • Authors:

  • F. J. Berlanga;A. J. Rivera;M. J. del Jesus;F. Herrera

  • Affiliations:

  • University of Zaragoza, Dept. of Computer Science and Systems Engineering, E-50018 Zaragoza, Spain;University of Jaén, Dept. of Computer Science, E-23071 Jaén, Spain;University of Jaén, Dept. of Computer Science, E-23071 Jaén, Spain;University of Granada, Dept. of Computer Science and Artificial Intelligence, E-18071 Granada, Spain

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.