Oversampling algorithm based on generative adversarial network
Zeyuan Wei, Yanyun Fu, Wenxi Shi, Dong‐Xu Chen · 2023
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.