Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm

Authors:
Edmundo Bonilla Huerta;Béatrice Duval;Jin-Kao Hao
Affiliations:
LERIA, Université d'Angers, Angers, France 49045;LERIA, Université d'Angers, Angers, France 49045;LERIA, Université d'Angers, Angers, France 49045
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

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Abstract

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy. This paper introduces a new wrapper approach to this difficult task where a Genetic Algorithm (GA) is combined with Fisher's Linear Discriminant Analysis (LDA). This LDA-based GA algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA's discriminant coefficients in its dedicated crossover and mutation operators. The proposed algorithm is assessed on a set of seven well-known datasets from the literature and compared with 16 state-of-art algorithms. The results show that our LDA-based GA obtains globally high classification accuracies (81%-100%) with a very small number of genes (2-19).