Gene Selection in Cancer Classification using GPSO/SVM and GA/SVM Hybrid Algorithms

Alba, Enrique; Garcia-Nieto, José; Jourdan, Laetitia; Talbi, El-Ghazali

Type de document :

Communication dans un congrès avec actes

Titre :

Gene Selection in Cancer Classification using GPSO/SVM and GA/SVM Hybrid Algorithms

Auteur(s) :

Alba, Enrique [Auteur]
Departamento Lenguajes y Ciencias de la Computación [Malaga] [LCC]
Garcia-Nieto, José [Auteur]
Departamento Lenguajes y Ciencias de la Computación [Malaga] [LCC]
Jourdan, Laetitia [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Titre de la manifestation scientifique :

Congress on Evolutionary Computation

Ville :

Singapor

Pays :

Singapour

Date de début de la manifestation scientifique :

2007-09

Éditeur :

IEEE

Date de publication :

2007

Discipline(s) HAL :

Mathématiques [math]/Combinatoire [math.CO]

Résumé en anglais : [en]

In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high dimensional Microarray Data. Both algorithms ...
Lire la suite >In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high dimensional Microarray Data. Both algorithms are used for finding small samples of informative genes amongst thousands of them. A SVM classifier with 10- fold cross-validation is applied in order to validate and evaluate the provided solutions. A first contribution is to prove that PSOSVM is able to find interesting genes and to provide classification competitive performance. Specifically, a new version of PSO, called Geometric PSO, is empirically evaluated for the first time in this work using a binary representation in Hamming space. In this sense, a comparison of this approach with a new GASVM and also with other existing methods of literature is provided. A second important contribution consists in the actual discovery of new and challenging results on six public datasets identifying significant in the development of a variety of cancers (leukemia, breast, colon, ovarian, prostate, and lung).Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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