A NEW METHOD TO CONSTRUCT MEMBERSHIP FUNCTIONS AND GENERATE WEIGHTED FUZZY RULES FROM TRAINING INSTANCES

Abstract

Fuzzy classification systems are important applications of the fuzzy set theory. In order to design a fuzzy classification system, it is an important task to construct the membership function of each attribute and generate fuzzy rules that are suitable for handling a specific classification problem. In this paper, we propose a new method to construct the membership function of each attribute and generate weighted fuzzy rules from training instances for handling fuzzy classification problems. The proposed method can construct membership functions and generate weighted fuzzy rules without any human experts' intervention. It can get a higher average classification accuracy rate and generate fewer fuzzy rules than the existing methods.

ACKNOWLEDGMENTS

This work was supported in part by the National Science Council, Republic of China, under Grant NSC 89-2213-E-011-100.