Robust Variable Selection Technique for Single Index Support Vector Regression Model


The single index support vector regression model (SI-SVR) is a useful regression technique used to alleviate the problem of high-dimensionality. In this study, we propose a robust variable selection technique for the SI-SVR model by using vital method to identify and minimize the effects of outliers in the data set. The effectiveness of the proposed robust variable selection technique of the SI-SVR model is explored by using various simulation examples. Furthermore, the suggested method is tested by analyzing a real data set which highlights the utility of the proposed methodology.