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Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy
Chen, Hui Ling1,2,4; Yang, Bo3,4; Wang, Su Jing5; Wang, Gang3; Liu, Da You3,4; Li, Huai Zhong1,2; Liu, Wen Bin1,2
2014-07-15
Source PublicationAPPLIED MATHEMATICS AND COMPUTATION
ISSN0096-3003
SubtypeArticle
Volume239Issue:0Pages:180-197
AbstractProper 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.
KeywordSupport Vector Machines Particle Swarm Optimization Parallel Computing Feature Selection
Indexed BySCI
Language英语
WOS IDWOS:000336844300016
Citation statistics
Cited Times:21[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/14139
Collection脑与认知科学国家重点实验室
Affiliation1.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
Recommended Citation
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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