Optimizing the Fuzzy Classification System through Genetic Algorithm

Authors:
Jong Ryul Kim;Do-Un Jeong
Affiliations:
-;-
Venue:
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 02
Year:
2008

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Abstract

This paper tries to apply a genetic algorithm-based method to fuzzy rule-base system for fuzzy classification with minimum fuzzy rules, which simultaneously enhances or maintain the performance of the fuzzy classification system with fuzzy rule-base. That is, the optimization is included with the minimization of the number of the extracted fuzzy rules and the maximization of the performance of the fuzzy classification system, i.e., the number of correctly classified training patterns with the results fuzzy rules. In optimization process, we also try to apply some experiments in order to employ more suitable reasoning method of the selected fuzzy rules. Finally, we demonstrate with numerical experiments. From the results, we can see that our method is effective and efficient with respect to the number of the correctly classified patterns and the number of the used fuzzy rules in the fuzzy classification systems.