PT Journal
AU Miguel Angel Bautista
   Sergio Escalera
   Xavier Baro
   Petia Radeva
   Jordi Vitria
   Oriol Pujol
TI Minimal Design of Error-Correcting Output Codes
SO Pattern Recognition Letters
JI PRL
PY 2011
BP 693
EP 702
VL 33
IS 6
DI 10.1016/j.patrec.2011.09.023
DE Multi-class classification; Error-correcting output codes; Ensemble of classifiers
AB 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.
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