Hierarchical Oversampling Based on Cohen’s Criterion for Imbalanced Data With Missing Information

The generative adversarial network (GAN) is increasingly used to address data imbalanced. However, GAN struggle with minority data that have few instances and lack accurate statistical characteristics. To mitigate this problem, a hierarchical oversampling based on Cohen’s criterion (HiOC) is proposed for extremely imbalanced data with missing information. The main idea of HiOC is to use Cohen’s criterion to design a primary rebalancing rate by considering data size, distribution, and feature information comprehensively. Then, HiOC including two stages to achieve the data balance. In the first stage, to enhance the data variety, especially for instances on the borderline as well as make a delicate imputation for missing information, a progressive oversampling method in the framework of fuzzy information decomposition (FID) is proposed. The progressive FID (PFID) introduces a small bias to expand the prediction region’s bounds and fulfills imputation step by step using newly sampled data. In the second stage, to greatly keep the original data distribution, an information granularity (IG) incorporated fuzzy c-means clustering strategy is developed. Afterward, a GAN based on Mahalanobis distance performs oversampling in each cluster to achieve ultimate data balance with unbiased evaluation. Finally, the proposed algorithm is applied to several real datasets, demonstrating higher classification accuracy compared with existing algorithms, thus proving the applicability of the research results in real-world scenarios.