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

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...

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Bibliographic Details
Main Authors: Chua Teck Wee, Tan Woei Wan
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Gallium
Support vector machine classification
Support vector machines
Testing
Training
Online Access:Request full text
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Summary: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.
ISSN:1098-7584
DOI:10.1109/FUZZY.2008.4630431