Fast Forward Feature Selection by Analyzing Class Regions Approximated by Ellipsoids

Abstract

In our previous work, we have developed the backward selection method based on class regions approximated by ellipsoids. In this paper, we accelerate feature selection by the forward selection search, the symmetric Cholesky factorization, and deletion of duplicated calculations between consecutive factorizations. The feature selection for four data sets shows that our method is faster than and as robust as the previous method.