Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms

Abstract

Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomarkers for disease diagnosis and assessing drug efficiency. In this paper, we present an approach using a genetic algorithm for a feature subset selection problem that can be used in selecting the near optimum set of genes for classification of cancer data. In substantial improvement over existing methods, we classified cancer data with high accuracy with less features.