Feature Selection Methods on Biological Knowledge Discovery and Data Mining: A Survey

Abstract

Feature selection is an important component of data mining and knowledge discovery process, due to the availability of data with hundreds of variables leading to data with very high dimension. It aims at reducing the number of features by removing irrelevant or redundant ones, while trying to reduce computation time, preserve or improve prediction performance, and to a better understanding of the data in machine learning or pattern recognition and specific in bioinformatics applications where the number of features is significantly larger than the number of samples. In this paper we provide an overview of some feature selection methods present in literature. We focus on Filter, Wrapper and hybrid methods. We also apply some of the feature selection techniques on standard databank to demonstrate their applicability.