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

Dessì, Nicoletta; Pes, Barbara

doi:https://doi.org/10.1155/2009/803973

Journal of Artificial Evolution and Applications

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Artificial Evolution Methods in the Biological and Biomedical Sciences

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Research Article | Open Access

Volume 2009 | Article ID 803973 | https://doi.org/10.1155/2009/803973

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

Nicoletta Dessì¹and Barbara Pes¹

Academic Editor: Stephen Smith

Received20 Nov 2008

Accepted01 Jun 2009

Published22 Jul 2009

Abstract

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.

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Copyright

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.

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