B2BGAN: A Backbone-to-Branches GAN-Based Oversampling Approach for Class-Imbalanced Tabular Data

Abstract

Tabular data is prevalent in many fields. In practice, tabular data classification may encounter severe challenges due to class imbalance, i.e., some majority classes overwhelm minority ones. Such imbalance could lead to biased prediction tendency of trained classifiers towards majority classes. Oversampling minority classes is an essential solution due to its generality and independence of downstream tasks. Recent years have witnessed the advantages of generative adversarial networks (GANs) in synthetic data generation, favored for their ability to generate quasi-realistic samples. However, challenges arise when the size of minority classes is too small to provide sufficient information for learning real data distributions. Furthermore, the generated minority-class samples could exacerbate the class overlap problem, i.e., some generated samples unexpectedly overlap with partial majority-class samples. To address these challenges, this paper presents B2BGAN, a novel GAN-based approach for oversampling imbalanced tabular data. To capture the real data distribution in a fine-grained manner, we propose a novel backbone-to-branches neural network for the generator to fit the majority and minority classes simultaneously. The backbone network fits the whole distribution of the entire data, while each branch network grasps the distinctive characteristics of individual classes. To alleviate the class overlap problem of generated samples, we develop a prototype-guided loss function to ensure that generated samples are closer to the corresponding class prototypes. We evaluate the effectiveness of B2BGAN on six real-world datasets using six metrics. Experimental results demonstrate that our method outperforms state-of-the-art models by 5.38% in AUC and 10.19% in AP.