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In recent years, classifier ensembles have received increasing attention in the machine learning and pattern recognition communities. However, constructing classifier ensembles for one-class classification problems has still remained as a challenging research topic. To pursue this line of research, we need to address issues on how to generate a set of diverse one-class classifiers that are individually accurate and how to combine the outputs of them in an effective way. In this paper, we present BeeOWA, a novel approach to construct highly accurate one-class classifier ensembles. It uses a novel binary artificial bee colony algorithm, called BeePruner, to prune an initial one-class classifier ensemble and find a near-optimal sub-ensemble of base classifiers in a reasonable computational time. To evaluate the fitness of an ensemble solution, BeePruner uses two different measures: an exponential consistency …