A Tutorial on Generative Adversarial Networks with Application to Classification of Imbalanced Data.
A Tutorial on Generative Adversarial Networks with Application to Classification of Imbalanced Data.
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DOI：
10.1002/sam.11570
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发表时间：
2022-10
影响因子：
1.3
通讯作者：
Ma, Yan
中科院分区：
文献类型：
--
作者：
Huang, Yuxiao;Fields, Kara G.;Ma, Yan
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，当一个类别（少数群体）包含的样本明显少于另一个类别（多数群体）时，就会发生类别不平衡。在不平衡数据中，少数类通常是感兴趣的类（例如，有疾病的患者）。然而，当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，在极端情况下，可能会完全忽略少数类。解决类不平衡的一个常见策略是数据扩充。然而，传统的数据增强方法与过拟合相关联，其中模型拟合数据中的噪声。在本教程中，我们将介绍一种先进的数据增强方法：生成对抗网络（GAN）。GANs相对于传统数据增强方法的优势使用乳腺癌威斯康星州研究来说明。为了促进GAN在数据增强中的采用，我们提出了一个端到端的管道，它包含了机器学习项目的完整生命周期，沿着了论文和单独视频中的替代方案和良好实践。我们的代码，数据，完整的结果和视频教程在论文的github repositoryd中公开。
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 videod. Our code, data, full results and video tutorial are publicly available in the paper’s github repositoryd.
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DOI：
10.1093/geronb/55.3.s173
发表时间：
2000-05-01
影响因子：
6.2
作者：
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通讯作者：
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