TY  - CHAP
AU  - Miguel Angel Bautista
AU  - Sergio Escalera
AU  - Xavier Baro
AU  - Oriol Pujol
AU  - Jordi Vitria
AU  - Petia Radeva
PY  - 2011//
TI  - On the Design of Low Redundancy Error-Correcting Output Codes
BT  - Ensembles in Machine Learning Applications
SP  - 21
EP  - 38
VL  - 373
IS  - 2
PB  - Springer Berlin Heidelberg
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.
SN  - 1860-949X
SN  - 978-3-642-22909-1
UR  - https://springerlink3.metapress.com/content/tnw42p4x73722447/resource-secured/?target=fulltext.pdf&sid=jwvtkmgshuldloa43qtukhp5&sh=www.springerlink.com
UR  - http://dx.doi.org/10.1007/978-3-642-22910-7_2
N1  - MILAB; OR;HuPBA;MV
ID  - Miguel Angel Bautista2011
ER  -