@article {IOPORT.06090778,
    author       = {Gao, Runpeng and San, Ye},
    title        = {A reduced least squares support vector machine optimized by differential evolution.},
    year         = {2011},
    journal      = {Journal of Harbin Engineering University},
    volume       = {32},
    number       = {8},
    issn         = {1006-7043},
    pages        = {1012-1018},
    publisher    = {Editorial Board of Journal of Harbin Engineering University, Harbin, Heilongjiang},
    doi          = {10.3969/j.issn.1006-7043.2011.08.009},
    abstract     = {Summary: Aiming at lack of sparseness of the solutions of least squares support vector regression machines, which leads to slow prediction speed and other problems, the vector correlation analysis is 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 are taken as the novel performance evaluation criterion. Discrete addition, subtraction and multiplication operator are defined and the novel performance evaluation criterion is used as a fitness function. The best reduced model globally optimized by integer coded differential evolution algorithm can 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 criteria presented before. And reduced model obviously decreases support vectors at cost of little generalization performance.},
    identifier   = {06090778},
}