Class imbalanced problems (CIP) are one of the potential challenges in
developing unbiased Machine Learning (ML) models for predictions. CIP occurs
when data samples are not equally distributed between the two or multiple
classes. Borderline-Synthetic Minority Oversampling Techniques (SMOTE) is one
of the approaches that has been used to balance the imbalance data by
oversampling the minor (limited) samples. One of the potential drawbacks of
existing Borderline-SMOTE is that it focuses on the data samples that lay at
the border point and gives more attention to the extreme observations,
ultimately limiting the creation of more diverse data after oversampling, and
that is the almost scenario for the most of the borderline-SMOTE based
oversampling strategies. As an effect, marginalization occurs after
oversampling. To address these issues, in this work, we propose a hybrid
oversampling technique by combining the power of borderline SMOTE and
Generative Adversarial Network to generate more diverse data that follow
Gaussian distributions. We named it BSGAN and tested it on four highly
imbalanced datasets: Ecoli, Wine quality, Yeast, and Abalone. Our preliminary
computational results reveal that BSGAN outperformed existing borderline SMOTE
and GAN-based oversampling techniques and created a more diverse dataset that
follows normal distribution after oversampling effect.

本文提出了一种结合 Borderline-Synthetic Minority Oversampling Techniques 和 Generative Adversarial Network 的混合过采样技术 (BSGAN)，并在四个高度不平衡的数据集上进行了测试，结果表明 BSGAN 在过采样效应后创建了一个更多元化的数据集，其表现优于存在的边界 SMOTE 和基于 GAN 的过采样技术。