BeeOWA: A novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles

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 measure and a non-pairwise diversity measure based on the Kappa inter-rater agreement. After one-class classifier pruning, BeeOWA uses a novel exponential induced OWA (ordered weighted averaging) operator, called EIOWA, to combine the outputs of base classifiers in the sub-ensemble. The results of experiments carried out on a number of benchmark datasets show that BeeOWA can outperform several state-of-the-art approaches, both in terms of classification performance and statistical significance.