A subspace approach to error correcting output codes

Authors:
Mohammad Ali Bagheri;Gholam Ali Montazer;Ehsanollah Kabir
Affiliations:
Department of Information Technology, School of Engineering, Tarbiat Modares University, Tehran, Iran and Research Assistant at Faculty of Computer Science, Dalhousie University, Halifax, Canada;Department of Information Technology, School of Engineering, Tarbiat Modares University, Tehran, Iran;Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Venue:
Pattern Recognition Letters
Year:
2013

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Abstract

Among the proposed methods to deal with multi-class classification problems, the error-correcting output codes (ECOCs) represents a powerful framework. A 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 classical ECOC design in order to improve independency among classifiers. The main idea of the proposed method is based on using different feature subsets for each binary classifier, named subspace ECOC. In addition to creating more independent classifiers in the proposed technique, ECOC matrices with longer codes can be built. The numerical experiments in this study compare the classification accuracy of subspace ECOC, classical ECOC, one-versus-one, and one-versus-all methods over a set of UCI machine learning repository datasets and two image vision applications. The results show that the proposed technique increases the classification accuracy in comparison with the state of the art coding methods.