Novel Feature Selection Method for Nonlinear Support Vector Regression

Abstract

The development of sparse techniques presents a major challenge to complex nonlinear high‐dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS‐NSVR, which first attempts to solve the nonlinear feature selection problem in the regression technology field. FS‐NSVR preserves the representative features selected in the complex nonlinear system due to its use of a feature selection matrix in the original space. FS‐NSVR is a challenging mixed‐integer programming problem that is solved efficiently by using an alternate iterative greedy algorithm. Experimental results on three artificial datasets and five real‐world datasets confirm that FS‐NSVR effectively selects representative features and discards redundant features in a nonlinear system. FS‐NSVR outperforms L 1 ‐norm support vector regression, L 1 ‐norm least squares support vector regression, and L p ‐norm support vector regression on both feature selection ability and regression efficiency.