针对表格数据不平衡与分布外检测难题，提出基于高斯描述符的新型检测方法。
DNN-GDITD: Out-of-distribution detection via Deep Neural Network based Gaussian Descriptor for Imbalanced Tabular Data
- 用球形决策边界结合三种损失函数提升分类与分布外检测能力
- 在多个数据集上相比三种基线算法表现更优，尤其在不平衡场景下
- 适用于金融、传感器等实际表格数据场景，对异常样本敏感
分类任务因类别不平衡和数据分布变化面临挑战。本文提出一种面向表格数据的新颖分布外（OOD）检测算法——基于深度神经网络的不平衡表格数据高斯描述符（DNN-GDITD）。该算法可部署于任意深度神经网络之上，利用球形决策边界实现对不平衡数据的更好分类及分布外样本检测。通过组合使用推送损失、得分损失和焦点损失，DNN-GDITD为测试样本分配置信度，将其划分为已知类别或分布外样本。在多个表格数据集上的广泛实验表明，DNN-GDITD在平衡与不平衡场景下均显著优于三种基准方法。测试涵盖合成金融争议数据集及公开的气体传感器、驾驶诊断、MNIST等数据集，验证了其在不同场景下的泛化能力与实用性。
原文摘要 · Abstract (English)
Classification tasks present challenges due to class imbalances and evolving data distributions. Addressing these issues requires a robust method to handle imbalances while effectively detecting out-of-distribution (OOD) samples not encountered during training. This study introduces a novel OOD detection algorithm designed for tabular datasets, titled Deep Neural Network-based Gaussian Descriptor for Imbalanced Tabular Data (DNN-GDITD). The DNN-GDITD algorithm can be placed on top of any DNN to facilitate better classification of imbalanced data and OOD detection using spherical decision boundaries. Using a combination of Push, Score-based, and focal losses, DNN-GDITD assigns confidence scores to test data points, categorizing them as known classes or as an OOD sample. Extensive experimentation on tabular datasets demonstrates the effectiveness of DNN-GDITD compared to three OOD algorithms. Evaluation encompasses imbalanced and balanced scenarios on diverse tabular datasets, including a synthetic financial dispute dataset and publicly available tabular datasets like Gas Sensor, Drive Diagnosis, and MNIST, showcasing DNN-GDITD's versatility.
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