Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-type data. This is usual for vital statistics data, in which the outcome incidence dictates the amount of positive observations. In this article, we developed a novel neural network-based oversampling method called actGAN (activation-specific generative adversarial network) that can derive useful synthetic observations in terms of increasing prediction performance in this context.

中文翻译：

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使用生成对抗网络对生命统计数据进行合成少数采样。

少数族裔过采样是一种用于调整不平衡数据类别之间比率的标准方法。但是，当将成熟的方法应用于类别分布极不平衡的数据和混合类型数据时，分类性能通常会适度提高。对于生命统计数据来说，这是很常见的，其中结果发生率决定了积极观察的数量。在本文中，我们开发了一种新颖的基于神经网络的过采样方法，称为actGAN（激活特定的生成对抗网络），该方法可以在提高预测性能的情况下得出有用的综合观察结果。