让表格数据的深度模型可解释，通过语义特征线性组合预测结果。
Towards Interpretable Deep Neural Networks for Tabular Data
- 用稀疏自编码器在隐空间学习单一语义特征字典
- 预测结果由可读的语义成分线性构成，性能媲美黑盒模型
- 适合需要透明决策的金融、医疗等场景
表格数据是金融、医疗等领域应用的基础。尽管专为表格数据设计的深度神经网络（DNN）具备优异的预测性能，但其缺乏可解释性，属于黑箱模型。本文提出XNNTab，一种基于稀疏自编码器（SAE）的神经架构，在用于预测的隐空间中学习一组单义性特征字典，并通过自动化方法为这些特征赋予人类可理解的语义。由此，预测结果可表示为语义明确的成分之线性组合。实验表明，XNNTab在性能上达到或超过当前最先进的黑箱神经模型和经典机器学习方法，同时实现完全可解释。
原文摘要 · Abstract (English)
Tabular data is the foundation of many applications in fields such as finance and healthcare. Although DNNs tailored for tabular data achieve competitive predictive performance, they are blackboxes with little interpretability. We introduce XNNTab, a neural architecture that uses a sparse autoencoder (SAE) to learn a dictionary of monosemantic features within the latent space used for prediction. Using an automated method, we assign human-interpretable semantics to these features. This allows us to represent predictions as linear combinations of semantically meaningful components. Empirical evaluations demonstrate that XNNTab attains performance on par with or exceeding that of state-of-the-art, black-box neural models and classical machine learning approaches while being fully interpretable.
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