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.