Diversity measures for one-class classifier ensembles
One-class classification is one of the most challenging topics in contemporary machine learning and not much attention had been paid to the task of creating efficient one-class ensembles. The paper deals with the problem of designing combined recognition system based on the pools of individual one-class classifiers. We propose the new model dedicated to the one-class classification and introduce novel diversity measures dedicated to it. The proposed model of an one class classifier committee may be used for single-class and multi-class classification tasks. The proposed measures and classification models were evaluated on the basis of computer experiments which were carried out on diverse set of benchmark datasets. Their results confirm that introducing diversity measures dedicated to one-class ensembles is a worthwhile research direction and prove that the proposed models are valuable propositions which can outperform the traditional methods for one-class classification.
diversity measure one-class classification machine learning multiple classifier system combined classifier