Hybridization of genetic and quantum algorithm for gene selection and classification of microarray data

Abstract : In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional Microarray Data. We named our algorithm GQASVM. Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy. A comparison of the approach with different methods of literature, in particular GASVM and PSOSVM [2], was realized on six different datasets issued of microarray experiments dealing with cancer (leukemia, breast, colon, ovarian, prostate, and lung) and available on Web. The experiments clearified the very good performances of the method. The first contribution shows that the algorithm GQASVM is able to find genes of interest and improve the classification on a meaningful way. The second important contribution consists in the actual discovery of new and challenging results on datasets used.
Type de document :
Article dans une revue
International. Journal of Foundation of Computer Science, World Scientific, 2012, 23 (2), pp.431-444. 〈10.1142/S0129054112400217〉
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https://hal.inria.fr/hal-00750699
Contributeur : Talbi El-Ghazali <>
Soumis le : lundi 12 novembre 2012 - 10:08:27
Dernière modification le : vendredi 16 février 2018 - 16:24:03

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A. Allani, El-Ghazali Talbi, K. Mellouli. Hybridization of genetic and quantum algorithm for gene selection and classification of microarray data. International. Journal of Foundation of Computer Science, World Scientific, 2012, 23 (2), pp.431-444. 〈10.1142/S0129054112400217〉. 〈hal-00750699〉

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