@Article{MiguelAngelBautista2011,
author="Miguel Angel Bautista
and Sergio Escalera
and Xavier Baro
and Petia Radeva
and Jordi Vitria
and Oriol Pujol",
title="Minimal Design of Error-Correcting Output Codes",
journal="Pattern Recognition Letters",
year="2011",
publisher="Elsevier",
volume="33",
number="6",
pages="693--702",
optkeywords="Multi-class classification",
optkeywords="Error-correcting output codes",
optkeywords="Ensemble of classifiers",
abstract="IF JCR CCIA 1.303 2009 54/103The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.",
optnote="MILAB; OR;HuPBA;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1800), last updated on Tue, 11 Mar 2014 15:37:40 +0100",
issn="0167-8655",
doi="10.1016/j.patrec.2011.09.023"
}