On the use of different base classifiers in multiclass problems

Abstract

Classification problems with more than two classes can be handled in different ways. The most used approach is the one which transforms the original multiclass problem into a series of binary subproblems which are solved individually. In this approach, should the same base classifier be used on all binary subproblems? Or should these subproblems be tuned independently? Trying to answer this question, in this paper we propose a method to select a different base classifier in each subproblem—following the one-versus-one strategy—making use of data complexity measures. The experimental results on 17 real-world datasets corroborate the adequacy of the method.