Adversarial Training Classifier for Imbalanced and Semi-Supervised Learning

The imbalanced learning research has made great progress due to the introduction of generative adversarial networks (GANs). However, most studies focus on combining GAN models with oversampling techniques which could potentially compromise the distribution of the original training dataset. This study presents a cost-sensitive classifier based on the adversarial training framework that can not only deal with imbalanced data distribution but also utilize the unlabeled sample sets for semi-supervised learning. Extensive experiments are carried out to compare the classification performance on imbalanced datasets with the resampling and balanced ensemble method, as well as class-imbalanced semi-supervised scenarios, to demonstrate the advantages of the proposed method.