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

  • Jose Crispin Hernandez Hernandez
  • Béatrice Duval
  • Jin-Kao Hao
Conference paper

DOI: 10.1007/978-3-540-71783-6_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4447)
Cite this paper as:
Hernandez Hernandez J.C., Duval B., Hao JK. (2007) A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data. In: Marchiori E., Moore J.H., Rajapakse J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jose Crispin Hernandez Hernandez
    • 1
  • Béatrice Duval
    • 1
  • Jin-Kao Hao
    • 1
  1. 1.LERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045 AngersFrance

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