Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation

Abstract

Ensembling techniques have already been considered for improving the accuracy of k-nearest neighbor classifier. It is shown that using different feature subspaces for each member classifier, strong ensembles can be generated. Although it has a more flexible structure which is an obvious advantage from diversity point of view and is observed to provide better classification accuracies compared to voting based k-NN classifier, ensembling evidential k -NN classifier which is based on Dempster–Shafer theory of evidence is not yet fully studied. In this paper, we firstly investigate improving the performance of evidential k-NN classifier using random subspace method. Taking into account its potential to be perturbed also in parameter dimension due to its class and classifier dependent parameters, we propose ensembling evidential k-NN through multi-modal perturbation using genetic algorithms. Experimental results have shown that the improved accuracies obtained using random subspace method can be further surpassed through multi-modal perturbation.