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	Multi-objective parameters selection for SVM classification using NSGA-II
	Xu, Li ; Li, Chunping
	2010-05-07 ; 2010-05-07
会议名称	ADVANCES IN DATA MINING - APPLICATIONS IN MEDICINE, WEB MINING, MARKETING, IMAGE AND SIGNAL MINING ; 6th Industrial Conference on Data Mining (ICDM 2006) ; Leipzig, GERMANY ; Web of Science ; INSPEC
关键词	SUPPORT VECTOR MACHINES Computer Science, Artificial Intelligence
中文摘要	Selecting proper parameters is an important issue to extend the classification ability of Support Vector Machine (SVM), which makes SVM practically useful. Genetic Algorithm (CA) has been widely applied to solve the problem of parameters selection for SVM classification due to its ability to discover good solutions quickly for complex searching and optimization problems. However, traditional CA in this field relys on single generalization error bound as fitness function to select parameters. Since there have several generalization error bounds been developed, picking and using single criterion as fitness function seems intractable and insufficient. Motivated by the multi-objective optimization problems, this paper introduces an efficient method of parameters selection for SVM classification based on multi-objective evolutionary algorithm NSCA-II. We also introduce an adaptive mutation rate for NSGA-II. Experiment results show that our method is better than single-objective approaches, especially in the case of tiny training sets with large testing sets.
会议录出版者	SPRINGER-VERLAG BERLIN ; BERLIN ; HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
语种	英语 ; 英语
内容类型	会议论文
源URL	[http://hdl.handle.net/123456789/17052]
专题	清华大学
推荐引用方式 GB/T 7714	Xu, Li,Li, Chunping. Multi-objective parameters selection for SVM classification using NSGA-II[C]. 见:ADVANCES IN DATA MINING - APPLICATIONS IN MEDICINE, WEB MINING, MARKETING, IMAGE AND SIGNAL MINING, 6th Industrial Conference on Data Mining (ICDM 2006), Leipzig, GERMANY, Web of Science, INSPEC.

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