Weighted One-Class Classifier Ensemble Based on Fuzzy Feature Space Partitioning

This paper introduces a novel method for forming efficient one-class classifier ensembles. A common problem in one-class classification is a complex structure of the target class, which often leads to creation of a too expanded decision boundary. We propose to employ a clustering step in order to partition the target class into atomic subsets and using these as input for one-class classifiers. By this, we are able to detect sub-structures in the target concept. Additionally, to increase the diversity and robustness of our method weighted one-class classifiers are used. We introduce a novel scheme for calculating weights for training objects. Membership functions, obtained from the fuzzy clustering, are used to initialize the weighted classifiers. Based on the results of a number of computational experiments we show that the proposed method outperforms both the single one-class methods, as well as popular one-class ensembles. 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.