Parameter optimization of LS-SVM for regression using NGA

Qi Wang; Zhigang Feng; Katsunori Shida

doi:10.1117/12.718893

30 April 2007 Parameter optimization of LS-SVM for regression using NGA

Qi Wang, Zhigang Feng, Katsunori Shida

Proceedings Volume 6560, Intelligent Computing: Theory and Applications V; 65600A (2007) https://doi.org/10.1117/12.718893
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Compared with support vector machine (SVM), least squares support vector machine (LS-SVM) has overcame the shortcoming of higher computational burden by solving linear equations, and has been widely used in classification and nonlinear function estimation. But there is no efficient method for parameter selection of LS-SVM. In this paper, the sharing function based niche genetic algorithm (SNGA) is used to the parameter optimization of LS-SVM for regression. In the SNGA approach, k-folds cross validation is used to evaluate the LS-SVM generalization performance. The inverse of the average test error of the k trials is used as the fitness value. The hamming distance between each two individuals is defined as the sharing function. Two benchmark problems, SINC function regression and Henon map time series prediction are used as examples for demonstration. The results indicate that this approach can escape from the blindness of man-made choice of the LS-SVM parameters. It enhances the efficiency and the capability of regression. With little modification, this approach is also can be used to the parameter optimization of SVM or LS-SVM for classification.

Citation Download Citation

Qi Wang, Zhigang Feng, and Katsunori Shida "Parameter optimization of LS-SVM for regression using NGA", Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600A (30 April 2007); https://doi.org/10.1117/12.718893

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