Feature selection algorithms in classification problems: an experimental evaluation

Abstract

Feature selection (FS) is a significant topic for the development of efficient pattern recognition systems. FS refers to the selection of the most appropriate subset of features that describes (adequately) a given classification task. The objective of the present paper is to perform a thorough analysis of the performance and efficiency of feature selection algorithms (FSAs). The analysis covers a variety of important issues with respect to the functionality of FSAs, such as: (a) their ability to identify relevant features, (b) the performance of the classification models developed on a reduced set of features, (c) the reduction in the number of features and (d) the interactions between different FSAs with the techniques used to develop a classification model. The analysis considers a variety of FSAs and classification methods.