Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms

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 10fold 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 d...

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Enrique Alba, José García-Nieto, Laetitia Jourdan, El-Ghazali Talbi
• Keywords: Artificial Intelligence, CEC 2007, Particle Swarm Optimization, Support Vector Machines, SVM Classifier