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Communication dans un congrès

A memetic algorithm for gene selection and molecular classification of cancer

Abstract :

Choosing a small subset of genes that enables a good classification of diseases on the basis of microarray data is a difficult optimization problem. This paper presents a memetic algorithm, called MAGS, to deal with gene selection for supervised classification of microarray data. MAGS is based on an embedded approach for attribute selection where a classifier tightly interacts with the selection process. The strength of MAGS relies on the synergy created by combining a problem specific crossover operator and a dedicated local search procedure, both being guided by relevant information from a SVM classifier. Computational experiments on 8 well-known microarray datasets show that our memetic algorithm is very competitive compared with some recently published studies.

Type de document :
Communication dans un congrès
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Soumis le : mercredi 9 juin 2021 - 15:13:57
Dernière modification le : mercredi 20 octobre 2021 - 03:19:09

Lien texte intégral




Béatrice Duval, Jin-Kao Hao, Jose-Crispin Hernandez. A memetic algorithm for gene selection and molecular classification of cancer. 11th Annual conference on Genetic and evolutionary computation, 2009, Montréal, Canada. pp.201 - 208, ⟨10.1145/1569901.1569930⟩. ⟨hal-03255430⟩



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