RT Journal Article
T1 An ensemble approach of dual base learners for multi-class classification problems
A1 Sesmero Lorente, María Paz
A1 Alonso Weber, Juan Manuel
A1 Gutiérrez Sánchez, Germán
A1 Ledezma Espino, Agapito Ismael
A1 Sanchis de Miguel, María Araceli
AB In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient. (C) 2014 Elsevier B.V. All rights reserved.
PB Elsevier
SN 1566-2535
YR 2015
FD 2015-07-01
LK https://hdl.handle.net/10016/31872
UL https://hdl.handle.net/10016/31872
LA eng
NO This research was supported by the Spanish MICINN under Projects TRA2010-20225-C03-01, TRA 2011-29454-C03-02, and TRA 2011-29454-C03-03.
DS e-Archivo
RD 27 jul. 2024