Oversampling With GAN via Meta-Learning for Imbalanced Data

Utilizing generative adversarial networks (GANs) for oversampling imbalanced data has demonstrated its effectiveness. However, many GAN-based oversampling methods are confronted with a significant challenge, namely, mode collapse, especially when dealing with tabular imbalanced data. In this paper, two unique penalty terms are respectively incorporated into the loss functions of the discriminator and the generator of GAN to promote the generated samples to exhibit not just statistical but also spatial information consistency with the minority samples, thereby alleviating the issue of mode collapse. In contrast to other studies that fix the coefficient of the penalty terms, the optimal coefficients of the penalty terms are adaptively searched using a meta-learning approach, where Bayesian optimization is firstly employed to effectively handle situations involving small size of minority samples in the imbalanced data. We call the proposed model as META_GAN. Experimental results demonstrate that META_GAN outperforms alternative oversampling methods on general tabular and image imbalanced datasets and long-tailed datasets in terms of different metrics.