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EFSVM-FCM: Evolutionary fuzzy rule-based support vector machines classifier with FCM clustering

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2 Author(s)
Chua Teck Wee ; Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore ; Tan Woei Wan

This paper presents a hybrid TSK fuzzy rule-based classifier. Fuzzy c-means clustering and genetic algorithm and are used to optimize the number of rules and antecedent parameters. By using the relationship between a SVM and a TSK FLS, an efficient method for learning the consequent parts of the TSK fuzzy system is introduced. The resulting hybrid fuzzy classifier has a compact rule base and good generalization capabilities compared to existing algorithms in the literature. In this sense, the curse of dimensionality which is often associated with fuzzy rule-based classifier can be avoided. The performance of the proposed hybrid fuzzy classifier is verified through extensive tests and comparison with other methods.

Published in:

Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on

Date of Conference:

1-6 June 2008