Feature Selection Based on Twin Support Vector Regression

Abstract

Twin support vector regression (TSVR) is a regression algorithm based on the support vector regression (SVR) and the spirit of the support vector machine (TWSVM) . However, some feature selection algorithms of support vector regression, such as recursive feature elimination, can’t be applied to TSVR, so a recursive feature selection method based on TSVR is proposed. By analyzing the weights, the ε -insensitive upper and lower bound functions in TSVR are analyzed. The two weight vectors are merged, and the weight vector is sorted and deleted with reference to the recursive feature elimination (RFE). The experimental results on several UCI datasets demonstrate demonstrate the effectiveness of the algorithm on feature selection and improves the regression performance.