Máquinas de vetores suporte usando o algoritmo evolução diferencial com busca local para classificação de dados

Abstract

Data mining is a key area for many fields of science and engineering. In this context, a statistical learning method, known as Support Vector Machines has presented itself as a promising method to solve data classification. Usually, the SVM problem is formulated as a nonlinear optimization problem subject to constraints. Conventional optimization techniques using the Lagrangian approach are used to solve this kind of problem. In the case of classification of noisy data the conventional techniques show performance deterioration, since the resulting optimization problem is multidimensional and may present many local minima. In this work, it is proposed the Differential Evolution algorithm (DE) combined with a local search technique, a hybridization of tabu search with Nelder-Mead method, to find the optimal parameters of SVM classifiers applied to noisy data.