An genetic approach to Support Vector Machines in classification problems
Carlos Alberto de Araújo Padilha, Naiyan Hari Candido Lima, Adrião Duarte Dória Neto, Jorge Dantas de Melo · 2010
There are a lot of different methods in pattern classification, in which one of the most popular is the Support Vector Machine. Lots of tools have been developed to improve SVM classification, mainly the development of new classifying methods and the employment of SVM ensembles. Meanwhile, evolutionary algorithms are recognized tools to solve optimization problems, and have in the genetic algorithm its most popular metaheuristic. So, in this paper, our proposal is to unite both techniques, applying a genetic algorithm to optimize the classification of a set of SVM, testing with some benchmark data sets.