PT Chapter
AU Miguel Angel Bautista
   Sergio Escalera
   Xavier Baro
   Oriol Pujol
   Jordi Vitria
   Petia Radeva
TI On the Design of Low Redundancy Error-Correcting Output Codes
BT Ensembles in Machine Learning Applications
PY 2011
BP 21
EP 38
VL 373
IS 2
DI 10.1007/978-3-642-22910-7_2
AB The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the 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 of the 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 compact 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 compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.
ER