Constructing ensembles of classifiers using supervised projection methods based on misclassified instances

In this paper we propose an approach for ensemble construction based on the use of supervised projections, both linear and non-linear, to achieve both accuracy and diversity of individual classifiers. The proposed approach uses the philosophy of boosting, putting more effort on difficult instances, but instead of learning the classifier on a biased distribution of the training set, it uses misclassified instances to find a supervised projection that favors their correct classification. We show that supervised projection algorithms can be used for this task. We try several known supervised projections, both linear and non-linear, in order to test their ability in the present framework. Additionally, the method is further improved introducing concepts from oversampling for imbalance datasets. The introduced method counteracts the negative effect of a low number of instances for constructing the supervised projections. The method is compared with AdaBoost showing an improved performance on a large set of 45 problems from the UCI Machine Learning Repository. Also, the method shows better robustness in presence of noise with respect to AdaBoost.