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

BCE is an ensemble focused on multi-class problems with a large number of features.The base learners have a dual structure with a binary and a complementary classifier.To reduce the computational cost, BCE includes a Feature Selection module.BCE gives preference to the accuracy of the base learners over their diversity.BCE is at least as accurate as other classification methods but is more efficient. 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.