An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification. (7th July 2009)

• Record Type: Journal Article
• Authors: Dessì, Nicoletta; Pes, Barbara
• Other Names: Smith Stephen Academic Editor.
• Is Part Of: Journal of artificial evolution and applications. Volume 2009(2009)
• Publication Date: 2009-07-07
• Subjects: Evolutionary programming (Computer science) -- Periodicals; Evolutionary programming (Computer science); Periodicals; Electronic journals; 006.3823
• DOI: 10.1155/2009/803973
• Languages: English
• ISSNs: 1687-6229

Abstract

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.