International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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GA-Based Construction of Fuzzy Classifiers Using Information Granules

  • Kim Do-Wan (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Lee Ho-Jae (Department of Electronic Engineering, Inha University) ;
  • Park Jin-Bae (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Joo Young-Hoon (School of Electronic and Information Engineering, Kunsan National University)
  • Published : 2006.04.01
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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 is 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). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

Keywords

References

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