@InProceedings{SergioEscalera2009,
author="Sergio Escalera
and Oriol Pujol
and Petia Radeva",
title="Recoding Error-Correcting Output Codes",
booktitle="8th International Workshop of Multiple Classifier Systems",
year="2009",
publisher="Springer Berlin Heidelberg",
volume="5519",
pages="11--21",
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.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1190), last updated on Tue, 17 Dec 2013 16:34:19 +0100",
isbn="978-3-642-02325-5",
issn="0302-9743",
doi="10.1007/978-3-642-02326-2_2"
}