Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing
strategy for handling imbalanced data sets. Asymptotically, we prove that SMOTE
(with default parameter) regenerates the original distribution by simply
copying the original minority samples. We also prove that SMOTE density
vanishes near the boundary of the support of the minority distribution,
therefore justifying the common BorderLine SMOTE strategy. Then we introduce
two new SMOTE-related strategies, and compare them with state-of-the-art
rebalancing procedures. We show that rebalancing strategies are only required
when the data set is highly imbalanced. For such data sets, SMOTE, our
proposals, or undersampling procedures are the best strategies.

SMOTE 是处理不平衡数据集的常见重平衡策略，它能通过复制原始少数样本来重新生成原始分布，同时在少数分布边界附近 SMOTE 密度消失，验证了常见的边界 SMOTE 策略。我们还介绍了两种新的 SMOTE 相关策略，并将其与最先进的重平衡程序进行比较。我们表明，只有在数据集高度不平衡时才需要重平衡策略，对于这样的数据集，SMOTE、我们的提议或欠采样程序是最佳策略。

SMOTE 的理论和实验研究：再平衡策略的限制和比较

Theoretical and experimental study of SMOTE: limitations and comparisons  of rebalancing strategies

Given imbalanced data, it is hard to train a good classifier using deep
learning because of the poor generalization of minority classes. Traditionally,
the well-known synthetic minority oversampling technique (SMOTE) for data
augmentation, a data mining approach for imbalanced learning, has been used to
improve this generalization. However, it is unclear whether SMOTE also benefits
deep learning. In this work, we study why the original SMOTE is insufficient
for deep learning, and enhance SMOTE using soft labels. Connecting the
resulting soft SMOTE with Mixup, a modern data augmentation technique, leads to
a unified framework that puts traditional and modern data augmentation
techniques under the same umbrella. A careful study within this framework shows
that Mixup improves generalization by implicitly achieving uneven margins
between majority and minority classes. We then propose a novel margin-aware
Mixup technique that more explicitly achieves uneven margins. Extensive
experimental results demonstrate that our proposed technique yields
state-of-the-art performance on deep imbalanced classification while achieving
superior performance on extremely imbalanced data. The code is open-sourced in
our developed package this https URL to foster
future research in this direction.

通过增强传统的 SMOTE 方法并与 Mixup 结合，提出了一种统一的深度学习数据增强框架，实验结果表明该方法在深度不平衡分类任务上具有最先进的性能，并且在极度不平衡数据上也达到了优异的效果。