期刊
APPLIED SOFT COMPUTING
卷 8, 期 4, 页码 1381-1391出版社
ELSEVIER
DOI: 10.1016/j.asoc.2007.10.007
关键词
particle swarm optimization; support vector machines; distributed computing; web service; data mining; feature selection
资金
- National Science Council of the Republic of China, Taiwan [NSC 94-2213-E327-007]
This study proposed a novel PSO-SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO-SVM data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy. (C) 2007 Elsevier B.V. All rights reserved.
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