EFSVM-FCM: Evolutionary fuzzy rule-based support vector machines classifier with FCM clustering
Chua Teck Wee, Tan Woei Wan · 2008
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.