New Fuzzy Support Vector Machine Method Based on Entropy and Ant-Colony Optimization

Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation...

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Bibliographic Details
Published inApplied Mechanics and Materials Vol. 380-384; pp. 1580 - 1584
Main Authors Li, Xiao Xi, Chen, Hao Guang, Li, Da Xi
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 30.08.2013
Subjects
Fuzzy Support Vector Machine (FSVM)
Entropy
Genetic Algorithm (GA)
Clustering
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Summary:Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation of entropy and intelligence of GA, EGFSVM enhances the classification capability and makes clustering center more suitable and membership more accurate. Experimental results show EGFSVM has better precision and classification performance, especially to multi-class and large scale data.
Bibliography:Selected, peer reviewed papers from the 2013 International Conference on Vehicle & Mechanical Engineering and Information Technology (VMEIT 2013), August 17-18, 2013, Zhengzhou, Henan, China
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISBN:3037858206
9783037858202
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.380-384.1580