Ensemble Evidential Editing k-NNs through rough set reducts

Ensemble classifier is one among the machine learning hot topics and it has been successfully applied in many practical applications. Since the construction of an optimal ensemble remains an open and complex problem, several heuristics for constructing good ensembles have been introduced for several years now. One alternative consists of integrating rough set reducts into ensemble systems. To the best of our knowledge, almost existing methods neglect knowledge imperfection, knowing that several real world databases suffer from some kinds of uncertainty and incompleteness. In this paper, we develop an ensemble Evidential Editing k-Nearest Neighbors classfier (EEk-NN) through rough set reducts for addressing data with evidential attributes. Experimentations in some real databases have been carried out with the aim of comparing our proposal to another existing approach.