Ensemble of Classifiers Based on Hard Instances

There are several classification problems, which are difficult to solve using a single classifier because of the complexity of the decision boundary. Whereas, a wide variety of multiple classifier systems have been built with the purpose of improving the recognition process. There is no universal method performing the best. The aim of this paper is to show another model of combining classifiers. This model is based on the use of different classifier models. It makes clusters to divide the dataset, taking into account the performance of the base classifiers. The system learns how to decide from the groups, by a meta-classifier, who are the best classifiers for a given pattern. In order to compare the new model with well-known classifier ensembles, we carried out experiments with some international databases. The results demonstrate that this new model can achieve similar or better performance than the classic ensembles.