Isomorphic Wasserstein Generative Adversarial Network for Numeric Data Augmentation

Abstract

GAN-based schemes are one of the most popular methods designed for image generation. Some recent studies have suggested using GAN for numeric data augmentation that is to generate data for completing the imbalanced numeric data. Compared to the conventional oversampling methods, taken SMOTE as an example, the proposed GAN schemes fail to generate distinguishable augmentation result for classifiers. This paper introduces an isomorphic structure between generator G and discriminator D to the conventional WGAN, and hence develops an Isomorphic Wasserstein Generative Adversarial Networks (IWGAN). DGM-based analysis proves that the isomorphic structure establishes an additional restriction from D to G in learning G and verse vice. Hence, the isomorphic structure enhances the classification performance in AUC on four datasets on five classifiers compared with three other GANs, and the conventional SMOTE methods add up to 20 groups of experiments. IWGAN outperforms all others in 15/20 groups.