期刊
INFORMATION FUSION
卷 24, 期 -, 页码 122-136出版社
ELSEVIER
DOI: 10.1016/j.inffus.2014.09.002
关键词
Ensemble of classifiers; Multi-class classification; Artificial Neural Networks; Feature Selection; Diversity
资金
- Spanish MICINN [TRA2010-20225-C03-01, TRA 2011-29454-C03-02, TRA 2011-29454-C03-03]
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.
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