Class imbalance is a common issue in many applications such as medical diagnosis, fraud detection, web advertising, etc. Although standard deep learning method has achieved remarkably high-performance on datasets with balanced classes, its ability to classify imbalanced dataset is still limited. This paper proposes a novel end-to-end deep neural network architecture and adopts Gumbel distribution as an activation function in neural networks for class imbalance problem in the application of binary classification. Our proposed architecture, named GEV-NN, consists of three components: the first component serves to score input variables to determine a set of suitable input, the second component is an auto-encoder that learns efficient explanatory features for the minority class, and in the last component, the combination of the scored input and extracted features are then used to make the final prediction. We jointly optimize these components in an end-to-end training. Extensive experiments using real-world imbalanced datasets showed that GEV-NN significantly outperforms the state-of-the-art baselines by around 2% at most. In addition, the GEV-NN gives a beneficial advantage to interpret variable importance. We find key risk factors for hypertension, which are consistent with other scientific researches, using the first component of GEV-NN.

类不平衡是许多应用程序中的常见问题，例如医学诊断，欺诈检测，网络广告等。尽管标准深度学习方法在具有平衡类的数据集上已取得了显着的高性能，但其对不平衡数据集进行分类的能力仍然受到限制。本文提出了一种新颖的端到端深度神经网络架构，并在二进制分类的应用中采用Gumbel分布作为神经网络中的类不平衡问题的激活函数。我们提出的架构称为GEV-NN，它由三个组件组成：第一个组件用于对输入变量进行评分，以确定一组合适的输入；第二个组件是自动编码器，可为少数群体学习有效的解释功能；以及最后一个组成部分 然后将评分的输入和提取的特征的组合用于最终预测。我们在端到端培训中共同优化了这些组件。使用现实世界中的不平衡数据集进行的大量实验表明，GEV-NN的性能最多比最新基准高出2％左右。此外，GEV-NN具有解释变量重要性的有益优势。我们使用GEV-NN的第一个组成部分找到了与其他科学研究一致的高血压关键危险因素。GEV-NN具有解释变量重要性的有益优势。我们使用GEV-NN的第一个组成部分找到了与其他科学研究一致的高血压关键危险因素。GEV-NN具有解释变量重要性的有益优势。我们使用GEV-NN的第一个组成部分找到了与其他科学研究一致的高血压关键危险因素。