EviNAM让模型同时具备可解释性和准确不确定性估计。
EviNAM: Intelligibility and Uncertainty via Evidential Neural Additive Models
- 结合NAM的可解释性与证据学习，单次推理输出特征贡献和不确定性
- 在合成与真实数据上达到顶尖预测性能，同时给出精确的偶然与认知不确定性
- 适合需要可信决策支持的场景，如医疗或金融
可解释性与准确的不确定性估计对可靠决策至关重要。本文提出EviNAM，一种将神经加性模型（NAM）的可解释性与严谨不确定性估计相结合的证据学习扩展方法。与标准贝叶斯神经网络及以往证据方法不同，EviNAM可在一次前向传播中同时估计偶然不确定性与认知不确定性，并明确给出各特征的贡献值。在合成数据与真实数据上的实验表明，EviNAM达到了当前最先进的预测性能。尽管聚焦于回归任务，该方法可自然推广至分类与广义加性模型，为更可解释、更可信的预测提供新路径。
原文摘要 · Abstract (English)
Intelligibility and accurate uncertainty estimation are crucial for reliable decision-making. In this paper, we propose EviNAM, an extension of evidential learning that integrates the interpretability of Neural Additive Models (NAMs) with principled uncertainty estimation. Unlike standard Bayesian neural networks and previous evidential methods, EviNAM enables, in a single pass, both the estimation of the aleatoric and epistemic uncertainty as well as explicit feature contributions. Experiments on synthetic and real data demonstrate that EviNAM matches state-of-the-art predictive performance. While we focus on regression, our method extends naturally to classification and generalized additive models, offering a path toward more intelligible and trustworthy predictions.
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