Table of Contents
Journal of Artificial Evolution and Applications
Volume 2009, Article ID 803973, 10 pages
Research Article

An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Via Ospedale 72, 09124 Cagliari, Italy

Received 20 November 2008; Accepted 1 June 2009

Academic Editor: Stephen Smith

Copyright © 2009 Nicoletta Dessì and Barbara Pes. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.