A Filter Correlation Method for Feature Selection

Abstract

Biological data is undergoing exponential growth in volume and complexity. Often, the selection of biological features is a crucial step that aims to defy the curse of dimensionality to improve prediction performance in classification systems, facilitate viewing, understanding and analyzing data. In this paper we present an adaptation of the Fast Correlation Based Filter algorithm (FCBF) whose aims is to identify relevant, not redundant features to improve the capacity of prediction and reduce the search space.