哈尔滨工程大学学报2011，Vol.32Issue(8)：1012-1018,7.DOI:10.3969/j.issn.1006-7043.2011.08.009
基于差分进化优化的约简最小二乘支持向量机
Reduced least squares support vector machine optimized by differential evolution
摘要
Abstract
Aiming at lack of sparseness of the solutions of least squares support vector regression machine which leads to slow prediction speed and other problems, the vector correlation analysis was employed to reduce the support vectors in the high dimensional feature space. In order to make the reduced model best approximate the original one, sum squared prediction errors of training samples between the reduced model and original one were taken as the novel performance evaluation criterion. Discrete addition, subtraction and multiplication operator were defined and the novel performance evaluation criterion was used as fitness function. The best reduced model globally optimized by integer coded differential evolution algorithm could be obtained. The experimental results on four benchmark datasets show that reduced model obtained by the novel algorithm has better generalization performance, compared with the other three performance evaluation criterions presented before. And reduced model obviously decreases support vectors at cost of little generalization performance.关键词
最小二乘支持向量回归机/稀疏性/向量相关分析/差分进化/整数编码/支持向量约简Key words
least squares support vector regression machine/sparseness/vector correlation analysis/differential e-volution/integer coded/support vector reduction分类
信息技术与安全科学引用本文复制引用
高润鹏,伞冶..基于差分进化优化的约简最小二乘支持向量机[J].哈尔滨工程大学学报,2011,32(8):1012-1018,7.基金项目
国家自然科学基金资助项目(61074127). （61074127）
