Ponencia
Gene Selection in Cancer Classification using PSO/SVM and GA/SVM Hybrid Algorithms
Autor/es | Alba, Enrique
García Nieto, José Manuel Jourdan, Laetitia Talbi, El-Ghazali |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2007 |
Fecha de depósito | 2021-05-07 |
Publicado en |
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ISBN/ISSN | 978-1-4244-1339-3 1089-778X |
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. ... 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 PSOSV M 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 GASV M 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). |
Agencias financiadoras | Ministerio de Educación y Ciencia (MEC). España |
Identificador del proyecto | TIN2005-08818-C04-01 |
Cita | Alba, E., García Nieto, J.M., Jourdan, L. y Talbi, E. (2007). Gene Selection in Cancer Classification using PSO/SVM and GA/SVM Hybrid Algorithms. En CEC 2007: IEEE Congress on Evolutionary Computation (284-290), Singapore: IEEE Computer Society. |
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