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

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

Document type :

Communication dans un congrès avec actes

Title :

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

Author(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]

Conference title :

Congress on Evolutionary Computation

City :

Singapor

Country :

Singapour

Start date of the conference :

2007-09

Publisher :

IEEE

Publication date :

2007

HAL domain(s) :

Mathématiques [math]/Combinatoire [math.CO]

English abstract : [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 ...
Show more >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).Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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