RegCGAN: Resampling with Regularized CGAN for Imbalanced Big Data Problem

We consider the imbalanced data problem involving a new class of resampling-based models for classification. These models are variants of the conditional generative adversarial networks. An entropy regularization approach (RegCGAN) is employed to implement the corresponding imbalanced data learning. Its basic framework is introduced. Theoretical and simulation-based analyses are performed to demonstrate the existence and uniqueness of RegCGAN’s equilibrium point, and RegCGAN has excellent minority class prediction ability. We apply the results to two synthetically constructed and a real imbalanced dataset.