Deep learning meets oversampling: a learning framework to handle imbalanced classification

Abstract This research introduces a novel deep learning based oversampling method for the class imbalance problem. Compared to previous methods, our approach defines the oversampling process as a composition of multiple decisions. This allows the deep learning classifier to learn the optimal mechanism for each decision from the ground truth data patterns, enabling more fine-grained control over the data oversampling process. We provide experiments on real-world datasets to demonstrate the superiority of our solution over the state-of-the-art oversampling methods.