Simultaneous two-sample learning to address binary class imbalance problem in low-resource scenarios

Binary class imbalance problem refers to the scenario where the number of training samples in one class is much lower compared with the number of samples in the other class. This imbalance hinders the applicability of conventional machine learning algorithms to classify accurately. Moreover, many real world training datasets often fall in the category where data is not only imbalanced but also low-resourced. In this paper we introduce a novel...