Variational Autoencoder Based Synthetic Data Generation for Imbalanced Learning

Discovering pattern from imbalanced data plays an important role in numerous applications, such as health service, cyber security, and financial engineering. However, the imbalanced data greatly compromise the performance of most learning algorithms. Recently, various synthetic sampling methods have been proposed to balance the dataset. Although these methods have achieved great success in many datasets, they are less effective for high-dimensional data, such as the image. In this paper, we propose a variational autoencoder (VAE) based synthetic data generation method for imbalanced learning. VAE can produce new samples which are similar to those in the original dataset, but not exactly the same. We evaluate and compare our proposed method with the traditional synthetic sampling methods on various datasets under five evaluation metrics. The experimental results demonstrate the effectiveness of the proposed method.