TY - JOUR
T1 - On the design of low redundancy error-correcting output codes
AU - Bautista, Miguel Ángel
AU - Escalera, Sergio
AU - Baró, Xavier
AU - Pujol, Oriol
AU - Vitrià, Jordi
AU - Radeva, Petia
PY - 2011/10/24
Y1 - 2011/10/24
N2 - 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. © 2011 Springer-Verlag Berlin Heidelberg.
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. © 2011 Springer-Verlag Berlin Heidelberg.
U2 - 10.1007/978-3-642-22910-7_2
DO - 10.1007/978-3-642-22910-7_2
M3 - Article
VL - 373
SP - 21
EP - 38
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
SN - 1860-949X
ER -