Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy

	Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy
	Chen, Hui Ling 1,2,4; Yang, Bo 3,4; Wang, Su Jing 5; Wang, Gang 3; Liu, Da You 3,4; Li, Huai Zhong 1,2; Liu, Wen Bin 1,2
摘要	Proper parameter settings of support vector machine (SVM) and feature selection are of great importance to its efficiency and accuracy. In this paper, we propose a parallel time variant particle swarm optimization (TVPSO) algorithm to simultaneously perform the parameter optimization and feature selection for SVM, termed PTVPSO-SVM. It is implemented in a parallel environment using Parallel Virtual Machine (PVM). In the proposed method, a weighted function is adopted to design the objective function of PSO, which takes into account the average classification accuracy rates (ACC) of SVM, the number of support vectors (SVs) and the selected features simultaneously. Furthermore, mutation operators are introduced to overcome the problem of the premature convergence of PSO algorithm. In addition, an improved binary PSO algorithm is employed to enhance the performance of PSO algorithm in feature selection task. The performance of the proposed method is compared with that of other methods on a comprehensive set of 30 benchmark data sets. The empirical results demonstrate that the proposed method cannot only obtain much more appropriate model parameters, discriminative feature subset as well as smaller sets of SVs but also significantly reduce the computational time, giving high predictive accuracy. (C) 2014 Elsevier Inc. All rights reserved.
关键词	Support Vector Machines Particle Swarm Optimization Parallel Computing Feature Selection
	2014-07-15
语种	英语
发表期刊	APPLIED MATHEMATICS AND COMPUTATION
ISSN	0096-3003
卷号	239 期号:0 页码:180-197
期刊论文类型	Article
收录类别	SCI
WOS记录号	WOS:000336844300016
引用统计	被引频次：96[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/14139
专题	脑与认知科学国家重点实验室
作者单位	1.Wenzhou Univ, Coll Phys & Elect Informat, Wenzhou 325035, Zhejiang, Peoples R China 2.Wenzhou Univ, Coll Phys & Elect Informat, Wenzhou 325035, Zhejiang, Peoples R China 3.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China 4.Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China 5.Chinese Acad Sci, State Key Lab Brain & Cognit Sci, Inst Psychol, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Chen, Hui Ling,Yang, Bo,Wang, Su Jing,et al. Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy[J]. APPLIED MATHEMATICS AND COMPUTATION,2014,239(0):180-197.
APA	Chen, Hui Ling.,Yang, Bo.,Wang, Su Jing.,Wang, Gang.,Liu, Da You.,...&Liu, Wen Bin.(2014).Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy.APPLIED MATHEMATICS AND COMPUTATION,239(0),180-197.
MLA	Chen, Hui Ling,et al."Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy".APPLIED MATHEMATICS AND COMPUTATION 239.0(2014):180-197.

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