  In this paper, we propose a diversity-aware ensemble learning based
algorithm, referred to as DAMVI, to deal with imbalanced binary classification
tasks. Specifically, after learning base classifiers, the algorithm i)
increases the weights of positive examples (minority class) which are "hard" to
classify with uniformly weighted base classifiers; and ii) then learns weights
over base classifiers by optimizing the PAC-Bayesian C-Bound that takes into
account the accuracy and diversity between the classifiers. We show efficiency
of the proposed approach with respect to state-of-art models on predictive
maintenance task, credit card fraud detection, webpage classification and
medical applications.
