Journal
SCIENTIFIC PROGRAMMING
Volume 2016, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2016/2739621
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Funding
- Science and Technology Supporting Program, Sichuan Province, China [2013GZX0138, 2014GZ0154]
- Scientific Research Foundation for Young Teachers, Sichuan University [2015SCU11050]
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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.
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