Investigation of strategies for the generation of multiclass support vector machines

Support Vector Machines constitute a Machine Learning technique originally designed for the solution of two-class problems. This paper investigates and proposes strategies for the generalization of SVMs to problems with more than two classes. The focus of this work is on strategies that decompose the original multiclass problem into binary subtasks, whose outputs are combined. The proposed strategies aim to investigate the adaptation of the decompositions for each multiclass application considered, using information of the performance obtained in its solution or extracted from its examples. The implemented algorithms were evaluated using benchmark datasets and real applications from the Bioinformatics domain. Among the benefits observed is the obtainment of simpler decompositions, which require less binary classifiers in the multiclass solution.