A genetic embedded approach for gene selection and classification of microarray data

Authors:
Jose Crispin Hernandez Hernandez;Béatrice Duval;Jin-Kao Hao
Affiliations:
LERIA, Université d'Angers, Angers, France;LERIA, Université d'Angers, Angers, France;LERIA, Université d'Angers, Angers, France
Venue:
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2007

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Abstract

Classification of microarray data requires the selection of subsets of relevant genes in order to achieve good classification performance. This article presents a genetic embedded approach that performs the selection task for a SVM classifier. The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking information provided by the SVM classifier. The effectiveness of our approach is assessed using three well-known benchmark data sets from the literature, showing highly competitive results.