Variational autoencoder based synthetic data generation for imbalanced learning
Document Type
Conference Proceeding
Date of Original Version
2-2-2018
Abstract
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.
Publication Title, e.g., Journal
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume
2018-January
Citation/Publisher Attribution
Wan, Zhiqiang, Yazhou Zhang, and Haibo He. "Variational autoencoder based synthetic data generation for imbalanced learning." 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings 2018-January, (2018): 1-7. doi: 10.1109/SSCI.2017.8285168.