Please use this identifier to cite or link to this item: https://doi.org/10.1109/FUZZY.2008.4630431
Title: EFSVM-FCM: Evolutionary fuzzy rule-based support vector machines classifier with FCM clustering
Authors: Wee, C.T.
Wan, T.W. 
Issue Date: 2008
Source: Wee, C.T.,Wan, T.W. (2008). EFSVM-FCM: Evolutionary fuzzy rule-based support vector machines classifier with FCM clustering. IEEE International Conference on Fuzzy Systems : 606-612. ScholarBank@NUS Repository. https://doi.org/10.1109/FUZZY.2008.4630431
Abstract: 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. © 2008 IEEE.
Source Title: IEEE International Conference on Fuzzy Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/70118
ISBN: 9781424418190
ISSN: 10987584
DOI: 10.1109/FUZZY.2008.4630431
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