用稀疏自编码器让神经网络表格数据预测更可解释
XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders
- 用稀疏自编码器将复杂特征分解为单一语义的可解释项
- 在多个数据集上超越传统可解释模型，接近黑盒模型性能
- 适合需要透明决策的医疗、金融等高风险领域
在依赖表格数据且需可解释性的数据驱动应用中，决策树和线性回归等可解释模型被广泛使用。尽管神经网络具备更强的预测能力，但因其黑箱特性未被采用。本文提出XNNTab，一种结合神经网络表达力与可解释性的新架构：首先学习高度非线性的特征表示，再通过稀疏自编码器（SAE）将其分解为单义特征，并赋予人类可理解的概念，使整体模型预测具有内在可解释性。实验表明，XNNTab在多个基准数据集上优于现有可解释预测模型，且性能接近其不可解释的对应模型。
原文摘要 · Abstract (English)
In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neural networks can provide higher predictive performance, they are not used because of their blackbox nature. In this work, we present XNNTab, a neural architecture that combines the expressiveness of neural networks and interpretability. XNNTab first learns highly non-linear feature representations, which are decomposed into monosemantic features using a sparse autoencoder (SAE). These features are then assigned human-interpretable concepts, making the overall model prediction intrinsically interpretable. XNNTab outperforms interpretable predictive models, and achieves comparable performance to its non-interpretable counterparts.
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