Diversity in Ensembles for One-Class Classification

One-class classification, known also as learning in the absence of counterexamples, is one of the most challenging problems in the contemporary machine learning. The scope of the paper focuses on creating a one-class multiple classifier systems with diverse classifiers in the pool. An approach is proposed in which an ensemble of one-class classifiers, instead of a single one, is used for the target class recognition. The paper introduces diversity measures dedicated to the selection of such specific classifiers for the committee. Therefore the influence of heterogeneity assurance on the overall classification performance is examined. Experimental investigations prove that diversity measures for one-class classifiers are a promising research direction. Additionally the paper investigates the lack of benchmark datasets for one-class problems and proposes an unified approach for training and testing one-class classifiers on publicly available multi-class datasets.