Rough Set Subspace Error-Correcting Output Codes

Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera

Abstract: Among the proposed methods to deal with multi-class classification problems, the Error-Correcting Output Codes (ECOC) represents a powerful framework. The key factor in designing any ECOC matrix is the independency of the binary classifiers, without which the ECOC method would be ineffective. This paper proposes an efficient new approach to the ECOC framework in order to improve independency among classifiers. The underlying rationale for our work is that we design three-dimensional codematrix, where the third dimension is the feature space of the problem domain. Using rough set-based feature selection, a new algorithm, named "Rough Set Subspace ECOC (RSS-ECOC)" is proposed. We introduce the Quick Multiple Reduct algorithm in order to generate a set of reducts for a binary problem, where each reduct is used to train a dichotomizer. In addition to creating more independent classifiers, ECOC matrices with longer codes can be built. The numerical experiments in this study compare the classification accuracy of the proposed RSS-ECOC with classical ECOC, one-versus-one, and one-versus-all methods on 24 UCI datasets. The results show that the proposed technique increases the classification accuracy in comparison with the state of the art coding methods.