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Communication dans un congrès

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

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).

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Communication dans un congrès
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https://hal.univ-angers.fr/hal-03255429
Contributeur : Okina Université d'Angers <>
Soumis le : mercredi 9 juin 2021 - 15:13:55
Dernière modification le : jeudi 10 juin 2021 - 03:39:57

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Edmundo Bonilla Huerta, Béatrice Duval, Jin-Kao Hao. Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm. Third IAPR International Conference, PRIB 2008, 2008, Melbourne, Australia. pp.250 - 261, ⟨10.1007/978-3-540-88436-1_22⟩. ⟨hal-03255429⟩

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