Ponencia
Comparison of population based metaheuristics for feature selection: Application to microarray data classification
Autor/es | Talbi, El-Ghazali
Jourdan, Laetitia García Nieto, José Manuel Alba, Enrique |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2008 |
Fecha de depósito | 2021-05-06 |
Publicado en |
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ISBN/ISSN | 978-1-4244-1967-8 2161-5322 |
Resumen | 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 ... 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. 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). |
Cita | Talbi, E., Jourdan, L., García Nieto, J.M. y Alba, E. (2008). Comparison of population based metaheuristics for feature selection: Application to microarray data classification. En ACS/IEEE-AICCSA 2008: International Conference on Computer Systems and Applications (45-52), Doha, Qatar: IEEE Computer Society. |
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