Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

Volume 4140 of the series Lecture Notes in Computer Science pp 329-338

Evolutionary Training of SVM for Multiple Category Classification Problems with Self-adaptive Parameters

  • Ángel Kuri-MoralesAffiliated withDepartamento de Computación, Instituto Tecnológico Autónomo de Mèxico
  • , Iván Mejía-GuevaraAffiliated withPosgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, IIMAS

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We describe a methodology to train Support Vector Machines (SVM) where the regularization parameter (C) is determined automatically via an efficient Genetic Algorithm in order to solve multiple category classification problems. We call the kind of SVMs where C is determined automatically from the application of a GA a “Genetic SVM” or GSVM. In order to test the performance of our GSVM, we solved a representative set of problems by applying one-versus-one majority voting and one-versus-all winner-takes-all strategies. In all of these the algorithm displayed very good performance. The relevance of the problem, the algorithm, the experiments and the results obtained are discussed.