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Evolutionary Design of Fuzzy Classifiers Using Information Granules

  • Do Wan Kim
  • Jin Bae Park
  • Young Hoon Joo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes).

Keywords

Genetic Algorithm Feature Selection Fuzzy System Fuzzy Rule Information Granule 
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 2005

Authors and Affiliations

  • Do Wan Kim
    • 1
  • Jin Bae Park
    • 1
  • Young Hoon Joo
    • 2
  1. 1.Yonsei UniversitySeoulKorea
  2. 2.Kunsan National UniversityKunsan, ChunbukKorea

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