A heuristic weight-setting algorithm for robust weighted least squares support vector regression

Authors:
Wen Wen;Zhifeng Hao;Zhuangfeng Shao;Xiaowei Yang;Ming Chen
Affiliations:
College of Computer Science and Engineering, South China University of Technology, Guangzhou, China;National Mobile Communications Research Laboratory, Southeast University Nanjing, China;School of Mathematical Science, South China University of Technology, Guangzhou, China;School of Mathematical Science, South China University of Technology, Guangzhou, China;National Mobile Communications Research Laboratory, Southeast University Nanjing, China
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression

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Abstract

Firstly, a heuristic algorithm for labeling the “outlierness” of samples is presented in this paper. Then based on it, a heuristic weight-setting algorithm for least squares support vector machine (LS-SVM) is proposed to obtain the robust estimations. In the proposed algorithm, the weights are set according to the changes of the observed value in the neighborhood of a sample's input space. Numerical experiments show that the heuristic weight-setting algorithm is able to set appropriate weights on noisy data and hence effectively improves the robustness of LS-SVM.