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2009 WRI World Congress on Computer Science and Information Engineering
Simultaneous Feature Selection and LS-SVM Parameters Optimization Algorithm Based on PSO
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
For the feature selection and parameter optimization of LS-SVM, propose a At first, a population of Particles (feature subsets) was randomly generated, then the features and parameters are optimized by PSO algorithm. The experiments on the UCI database indicate that the proposed method can efficiently find the suitable feature subsets and LS-SVM parameters. Also, comparison are made against GALS-SVM and LS-SVM; and the results show that the proposed PSOLS-SVM outperform the others in classification performance.
Index Terms:
LS-SVM, feature selection, parameters optimization, partical swarm optimization algorithm
Citation:
Quan-Zhu Yao, Jie Cai, Jiu-Long Zhang, "Simultaneous Feature Selection and LS-SVM Parameters Optimization Algorithm Based on PSO," csie, vol. 5, pp.723-727, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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