Feature selection for a nonlinear classifier

Abstract

The nonlinear classifier is effective for many practical problems. We have already proposed a method for constructing a nonlinear classifier using Legendre polynomials and have obtained good results on many actual data. In this approach, a set of original features is first extended to a large number of new features in a nonlinear fashion and then some substantial features are chosen for the nonlinear classifier. In this study, we have improved the selection process in the second stage by using some conventional feature selection algorithms. In addition, important features were selected from the original features in the preprocessing stage. The reduction in the number of the original features permits the nonlinear classifier to use a higher degree of polynomials.