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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.
Computer Science and Information Engineering, 2009 WRI World Congress on (Volume:5 )
Date of Conference: March 31 2009-April 2 2009