Fast structural ensemble for One-Class Classification

Abstract

One of the most important issues of One-Class Classification (OCC) algorithm is how to capture the characteristics of the positive class. Existing structural or clustering based ensemble OCC algorithms build description models for every cluster of the training dataset. However, the introduction of clustering algorithm also causes some problems, such as the determination of the number of clusters and the additional computational complexity. In this paper, we propose Fast Structural Ensemble One-Class Classifier (FS-EOCC) which is a fast framework for converting a common OCC algorithm to structural ensemble OCC algorithm. FS-EOCC adopts two rounds of complementary clustering with fixed number of clusters. This number is calculated according to the number of training samples and the complexity of the base OCC algorithm. Each partition found in the previous step is used to train one base OCC model. Finally all base models are modularly aggregated to build the structural OCC model. Experimental results show that FS-EOCC outperforms existing structural or clustering based OCC algorithms and state-of-the-art non-structural OCC algorithms. The comparison of running time for these algorithms indicates that FS-EOCC is an efficient framework because the cost of converting a common OCC algorithm to a structural OCC algorithm is small and acceptable.