Collaborative Discrimination-Enabled Generative Adversarial Network (CoD-GAN) for the Data Augmentation in Imbalanced Classification

Classification models have been widely applied to detect anomalies in many real-world fields, such as manufacturing process monitoring and disease early detection. However, the classification models may suffer from the data imbalance issue, as the abnormal/unhealthy states are usually rare events in regular data collection. Imbalanced data may result in significant training bias, leading to unsatisfactory classification accuracy. Incorporating data augmentation techniques, such as the popular generative adversarial networks (GAN), is a common strategy to eliminate the data imbalanced issue in classification. However, the performance of most GAN-based approaches may be unsatisfactory when the size of available training samples is small. To address this issue in GAN, the paper develops a novel collaborative discrimination-enabled GAN (CoD-GAN) to enhance its discrimination robustness. With the proposed collaborative discrimination framework, CoD-GAN is able to perform discrimination more effectively when the available real data is limited. Thus, the synthesized samples will be more effective, and the classification accuracy can be improved. The effectiveness of the proposed CoD-GAN has been validated by both numerical simulation data and real-world dataset. The results have demonstrated that the proposed method can further improve the data augmentation capability of GAN for imbalanced data classification.