Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches

Abstract

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The pro-posed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrap-per model included, to large and high-dimensional domains. The experiments performed show that our two strategies are competitive and can select a small subset of features without degrading the classifica-tion error or the advantages of the strategies under study.