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A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine
Joint Authors
Chen, Zhi
Lin, Tao
Tang, Ningjiu
Xia, Xin
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability.
The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained.
In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM.
The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found.
In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error.
Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.
American Psychological Association (APA)
Chen, Zhi& Lin, Tao& Tang, Ningjiu& Xia, Xin. 2016. A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine. Scientific Programming،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1118170
Modern Language Association (MLA)
Chen, Zhi…[et al.]. A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine. Scientific Programming No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1118170
American Medical Association (AMA)
Chen, Zhi& Lin, Tao& Tang, Ningjiu& Xia, Xin. A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine. Scientific Programming. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1118170
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118170