An ensemble approach of dual base learners for multi-class classification problems Articles uri icon

publication date

  • July 2015

start page

  • 122

end page

  • 136

volume

  • 24

international standard serial number (ISSN)

  • 1566-2535

electronic international standard serial number (EISSN)

  • 1872-6305

abstract

  • 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.

keywords

  • feature-selection; neural-networks; pattern-classification; stacked generalization; learning algorithms; mutual information; classifiers; accuracy; rules