Robust Least Square Support Vector Regression for Contaminated Data Modeling

Runda Jia, Fuli Wang, Dakuo He, and Tong Gao


Outlier, robust, support vector regression, least squares, weight


Weighted least squares support vector machine (WLS-SVM) is a robust version of least squares support vector machine (LS-SVM). It adds weights on error variables to eliminate the influence of outliers. But the weights, which largely depend on the original regression errors from unweighted LS-SVM, might be unreliable for correcting the biased estimation of LS-SVM, especially for the training data set with large deviation outliers. In this paper, a two-stage weighting strategy is proposed. This approach derives from the idea of spatial rank of feature vector, and down-weights these large deviation outliers firstly. Then the weights are updated by these regression errors of WLS-SVM with the weights obtained in the first weighting stage. Finally, WLS-SVM is again employed to further improve the prediction performance. The effectiveness of the proposed robust LS-SVM is validated by two artificial data examples and a soft sensor modeling problem.

Important Links:

Go Back