Comparing Several Evaluation Functions in the Evolutionary Design of Multiclass Support Vector Machines

Abstract

Support Vector Machines were originally designed to solve two-class classification problems. When they are applied to multiclass classification problems, the original problem is usually decomposed into multiple binary sub- problems. Afterwards, individual classifiers are induced to solve each of these binary problems. To obtain the final multiclass prediction, the outputs of these binary classifiers generated are combined. Genetic Algorithms can be used to optimize the combination of binary classifiers, defining the decomposition according to the performance obtained in the multiclass problem solution. This paper investigates several evaluation functions that can be used in order to evaluate the performance of the decompositions evolved by genetic algorithms.