A challenge unique to classification model development is imbalanced data. In a binary classification problem, class imbalance occurs when one class, the minority group, contains significantly fewer samples than the other class, the majority group. In imbalanced data, the minority class is often the class of interest (e.g., patients with disease). However, when training a classifier on imbalanced data, the model will exhibit bias towards the majority class and, in extreme cases, may ignore the minority class completely. A common strategy for addressing class imbalance is data augmentation. However, traditional data augmentation methods are associated with overfitting, where the model is fit to the noise in the data. In this tutorial we introduce an advanced method for data augmentation: generative adversarial networks (GANs). The advantages of GANs over traditional data augmentation methods are illustrated using the Breast Cancer Wisconsin study. To promote the adoption of GANs for data augmentation, we present an end-to-end pipeline that encompasses the complete life cycle of a machine learning project along with alternatives and good practices both in the paper and in a separate video. Our code, data, full results and video tutorial are publicly available in the paper's GitHub repository (https://github.com/yuxiaohuang/research/tree/master/gwu/accepted/sam_2021).

中文翻译：

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生成对抗网络教程及其在不平衡数据分类中的应用

分类模型开发的一个独特挑战是数据不平衡。在二元分类问题中，当一个类（少数群体）包含的样本明显少于另一类（多数群体）时，就会发生类不平衡。在不平衡数据中，少数类别通常是感兴趣的类别（例如，患有疾病的患者）。然而，在不平衡数据上训练分类器时，模型会表现出对多数类的偏见，并且在极端情况下，可能完全忽略少数类。解决类别不平衡的常见策略是数据增强。然而，传统的数据增强方法与过度拟合相关，其中模型适合数据中的噪声。在本教程中，我们介绍了一种先进的数据增强方法：生成对抗网络（GAN）。威斯康星州乳腺癌研究说明了 GAN 相对于传统数据增强方法的优势。为了促进采用 GAN 进行数据增强，我们在论文和单独的视频中提出了一个端到端管道，其中包含机器学习项目的完整生命周期以及替代方案和良好实践。我们的代码、数据、完整结果和视频教程可在论文的 GitHub 存储库中公开获取（https://github.com/yuxiao Huang/research/tree/master/gwu/accepted/sam_2021）。