A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray Classification

TL;DR

This paper introduces a hybrid feature selection method combining filter and wrapper techniques to improve microarray classification accuracy and efficiency in gene subset selection.

Contribution

It proposes a novel combination of information gain filter and an improved binary particle swarm optimization wrapper for gene selection.

Findings

Fewer genes are needed for accurate classification.

The hybrid method outperforms individual approaches in accuracy.

Reduced computational time for gene subset selection.

Abstract

Gene expression data is widely used in disease analysis and cancer diagnosis. However, since gene expression data could contain thousands of genes simultaneously, successful microarray classification is rather difficult. Feature selection is an important pre-treatment for any classification process. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases classification accuracy. In this study, we applied the information gain method as a filter approach, and an improved binary particle swarm optimization as a wrapper approach to implement feature selection; selected gene subsets were used to evaluate the performance of classification. Experimental results show that by employing the proposed method fewer gene subsets needed to be selected and better classification accuracy could be obtained.