Robust least squares-support vector machines for regression with outliers

Abstract

In this study, the robust least square support vector machines for regression (RLS-SVMR) is proposed to deal with training data set with outliers. There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector regression (SVR) approach is used to filter out the outliers in the training data set. Due to the outliers in the training data set are removed, the concepts of robust statistic theory have no need to reduce the outlierpsilas effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the non-robust least squares support vector machines for regression (LS-SVMR) in the stage II. Consequently, the learning mechanism of the proposed approach is much easier than the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach is superior to the weighted LS-SVMR approach when the outliers are existed.