Evolutionary design of multiclass support vector machines

Abstract

Support Vector Machines constitute a Machine Learning technique originally designed for the solution of 2-class problems. For multiclass applications, several strategies divide the original problem into a set of binary subtasks, whose results are combined. This work introduces the use of Genetic Algorithms to determine binary decompositions of multiclass problems. Experimental results on benchmark and Bioinformatics multiclass datasets indicate the potential of the proposed approach, which is able to produce good multiclass solutions with the use of simple decompositions.