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Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics

Volume 4447 of the series Lecture Notes in Computer Science pp 90-101

A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data

  • Jose Crispin Hernandez HernandezAffiliated withLERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045 Angers
  • , Béatrice DuvalAffiliated withLERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045 Angers
  • , Jin-Kao HaoAffiliated withLERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045 Angers

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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.

Keywords

Microarray gene expression Feature selection Genetic Algorithms Support vector machines