VGAN-BL: imbalanced data classification based on generative adversarial network and biased loss

The purpose of imbalanced data classification is to solve the problem of unfair learning caused by the large difference in data distribution. Traditional classifiers are designed on the basis of balanced data, but the performance of imbalanced data will decline sharply. Therefore, balancing the majority class and minority class samples before classification is a popular strategy for solving imbalanced learning. Current methods for data balance mainly include oversampling and undersampling. However, the existing undersampling will face the problem of losing important sample information, while oversampling cannot effectively fit the global distribution and generate noise. In recent years, generative adversarial network (GAN) has shown great potential in fitting real sample distributions. Based on this, this paper proposes an improved GAN and biased loss combined model, namely VGAN-BL, to solve the learning problem under imbalanced conditions. In the improvement based on GAN, VAE is used to generate latent vectors with posterior distribution as the input of GAN, and KL similarity measurement loss is introduced into the generator to improve the quality of minority samples generated by GAN. In addition, we propose a biased loss definition method based on the discriminator to improve the performance of classifier. Experiments on 20 real datasets show that the classification performance of the proposed method is significantly improved compared with other advanced methods. The source code can be found here: https://github.com/universuen/VGAN-BL.