Dynamic classifier selection for one-class classification

Bartosz Krawczyk, Michal Wozniak

Abstract: Highlights • Introduction of dynamic classifier selection for one-class classification. • Three novel competence measures for one-class classifiers. • Gaussian approach for extending competence over the entire decision space. • Results indicating that dynamic selection is a good alternative to static ensembles. Abstract One-class classification is among the most difficult areas of the contemporary machine learning. The main problem lies in selecting the model for the data, as we do not have any access to counterexamples, and cannot use standard methods for estimating the classifier quality. Therefore ensemble methods that can use more than one model, are a highly attractive solution. With an ensemble approach, we prevent the situation of choosing the weakest model and usually improve the robustness of our recognition system. However, one cannot assume that all classifiers available in the pool are in general accurate – they may have local competence areas in which they should be employed. In this work, we present a dynamic classifier selection method for constructing efficient one-class ensembles. We propose to calculate the competencies of all classifiers for a given validation example and use them to estimate their competencies over the entire decision space with the Gaussian potential function. We introduce three measures of classifier’s competence designed specifically for one-class problems. Comprehensive experimental analysis, carried on a number of benchmark data and backed-up with a thorough statistical analysis prove the usefulness of the proposed approach.