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

作者：Paz Sesmero M; Alonso Weber Juan M; Gutierrez German; Ledezma Agapito; Sanchis Araceli

来源：Information Fusion, 2015, 24: 122-136.

DOI：10.1016/j.inffus.2014.09.002

• Ensemble of classifiers
• Multi-class classification
• Artificial Neural Networks
• Feature Selection
• Diversity

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.

• 出版日期2015-7