Oversampling algorithm based on generative adversarial network

Zeyuan Wei; Yanyun Fu; Wenxi Shi; Dongxu Chen

doi:10.1117/12.2684591

19 October 2023 Oversampling algorithm based on generative adversarial network

Zeyuan Wei, Yanyun Fu, Wenxi Shi, Dongxu Chen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093N (2023) https://doi.org/10.1117/12.2684591
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In supervised learning, standard algorithms are mostly designed to deal with balanced data classes, but it is inevitable to encounter imbalanced data classes in some situations. How to learn from imbalanced data is still a challenging problem. A common approach is to generate artificial data or duplicate existing classes to balance the class distribution. Many traditional algorithms can handle imbalanced classes, but they often fail to enhance enough features and tend to produce unnecessary noise points. In this paper, we propose an improved network based on generative adversarial networks (GANs) and improved K-means Smote oversampling method to oversample the data. This method replaces the noise input of GANs with the oversampled results, and then generates new oversampled data through adversarial networks. By conducting experiments on six datasets, we show that this method can effectively improve the classification results of classifiers on oversampled data.

Citation Download Citation

Zeyuan Wei, Yanyun Fu, Wenxi Shi, and Dongxu Chen "Oversampling algorithm based on generative adversarial network", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093N (19 October 2023); https://doi.org/10.1117/12.2684591

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