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

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 proposed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrapper 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 classification error or the advantages of the strategies under study.

► Feature selection and ranking are two key issues in pattern recognition and machine learning. ► We develop two hybrid strategies that directly search on a ranked list of features. ► The proposed hybrid strategies can handle large datasets. ► These strategies can select small feature subsets without degrading the classification error.