Clustering-based ensembles for one-class classification

This paper presents a novel multi-class classifier based on weighted one-class support vector machines (OCSVM) operating in the clustered feature space. We show that splitting the target class into atomic subsets and using these as input for one-class classifiers leads to an efficient and stable recognition algorithm. The proposed system extends our previous works on combining OCSVM classifiers to solve both one-class and multi-class classification tasks. The main contribution of this work is the novel architecture for class decomposition and combination of classifier outputs. Based on the results of a large number of computational experiments we show that the proposed method outperforms both the OCSVM for a single class, as well as the multi-class SVM for multi-class classification problems. Other advantages are the highly parallel structure of the proposed solution, which facilitates parallel training and execution stages, and the relatively small number of control parameters. (C) 2013 Elsevier Inc. All rights reserved.