Recoding Error-Correcting Output Codes

Authors:
Sergio Escalera;Oriol Pujol;Petia Radeva
Affiliations:
Computer Vision Center, Bellaterra, Spain 08193 and Dept. Matemàtica Aplicada i Anàlisi, UB, Barcelona, 08007;Computer Vision Center, Bellaterra, Spain 08193 and Dept. Matemàtica Aplicada i Anàlisi, UB, Barcelona, 08007;Computer Vision Center, Bellaterra, Spain 08193 and Dept. Matemàtica Aplicada i Anàlisi, UB, Barcelona, 08007
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

One of the most widely applied techniques to deal with multi- class categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and classified according to a comparison with the positions of the coded matrix. In this paper, we present a novel approach to redefine without retraining, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information increases the generalization capability of the system. Moreover, the final classification can be tuned with the inclusion of a weighting matrix in the decoding step. The approach has been validated over several UCI Machine Learning repository data sets and two real multi-class problems: traffic sign and face categorization. The results show that performance improvements are obtained when comparing the new approach to one of the best ECOC designs (one-versus-one). Furthermore, the novel methodology obtains at least the same performance than the one-versus-one ECOC design.