提出无需改造模型的动态特征选择方法，同时量化决策不确定性。
Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
- 通过重参数化策略实现与预训练模型兼容的动态选特征。
- 实测在表格和图像数据上性能媲美顶尖贪心与强化学习方法。
- 揭示新不确定性来源，适合高风险场景中需可信决策的用户。
动态特征选择（DFS）通过为每个实例逐次获取特征来应对决策中的资源限制，适用于资源受限场景。然而，现有方法需专门设计用于序列获取设置的模型，难以兼容已部署的实际模型。此外，其不确定性量化能力有限，影响高风险决策的信任度。本文指出，相比静态设置，DFS引入了新的不确定性来源：模型对特征子集的适应会带来认知不确定性；标准插补策略会扭曲随机不确定性估计；预测置信度无法区分优劣选择策略。为此，我们提出一种模型无关的DFS框架，通过高效的子集重参数化策略，兼容预训练分类器（包括可解释设计模型）。在表格和图像数据集上的实验表明，该方法在神经网络和规则类分类器下均达到与当前最优贪心及强化学习方法相当的准确率。进一步分析显示，这些不确定性来源普遍存在于多数现有方法中，凸显了构建不确定性感知的DFS的必要性。
原文摘要 · Abstract (English)
Dynamic feature selection (DFS) addresses budget constraints in decision-making by sequentially acquiring features for each instance, making it appealing for resource-limited scenarios. However, existing DFS methods require models specifically designed for the sequential acquisition setting, limiting compatibility with models already deployed in practice. Furthermore, they provide limited uncertainty quantification, undermining trust in high-stakes decisions. In this work, we show that DFS introduces new uncertainty sources compared to the static setting. We formalise how model adaptation to feature subsets induces epistemic uncertainty, how standard imputation strategies bias aleatoric uncertainty estimation, and why predictive confidence fails to discriminate between good and bad selection policies. We also propose a model-agnostic DFS framework compatible with pre-trained classifiers, including interpretable-by-design models, through efficient subset reparametrization strategies. Empirical evaluation on tabular and image datasets demonstrates competitive accuracy against state-of-the-art greedy and reinforcement learning-based DFS methods with both neural and rule-based classifiers. We further show that the identified uncertainty sources persist across most existing approaches, highlighting the need for uncertainty-aware DFS.
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