Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification

Combining traditional diversity and re-balancing techniques serves to design effective ensembles for solving imbalanced classification problems. Therefore, to explore the performance of new diversification procedures and new re-balancing methods is an attractive research subject which can provide even better performances. In this contribution, we propose to create ensembles of the recently introduced binary Bayesian classifiers, that show intrinsic re-balancing capacities, by means of a diversification mechanism which is based on applying different cost policies to each ensemble learner as well as appropriate aggregation schemes. Experiments with an extensive number of representative imbalanced datasets and their comparison with those of several selected high-performance classifiers show that the proposed approach provides the best overal results.