用特征模板库优化智能特征获取，降低代价并提升效率。
Information Templates: A New Paradigm for Intelligent Active Feature Acquisition
- 构建特征模板库，指导非贪婪的特征选择
- 在真实与合成数据上均优于现有方法，成本更低
- 适合需要高效特征采集的实时系统
主动特征获取（AFA）是一种在推理时动态选择需获取特征（有成本）的自适应范式。现有方法要么训练强化学习策略，面临复杂MDP问题；要么采用贪心策略，无法捕捉特征间的联合信息或依赖数据分布知识。为此，我们提出基于模板的AFA（TAFA），一种非贪心框架：学习一组小型特征模板——即具有联合信息量的特征集合——并以此指导后续特征获取。通过识别特征模板，该框架显著缩小策略的动作空间，同时减少对底层数据分布的估计需求。在合成与真实世界数据集上的大量实验表明，TAFA在性能上超越现有最优基线，且整体获取成本和计算开销更低。
原文摘要 · Abstract (English)
Active feature acquisition (AFA) is an instance-adaptive paradigm in which, at inference time, a policy sequentially chooses which features to acquire (at a cost) before predicting. Existing approaches either train reinforcement learning policies, which deal with a difficult MDP, or greedy policies that cannot account for the joint informativeness of features or require knowledge about the underlying data distribution. To overcome this, we propose Template-based AFA (TAFA), a non-greedy framework that learns a small library of feature templates -- sets of features that are jointly informative -- and uses this library of templates to guide the next feature acquisitions. Through identifying feature templates, the proposed framework not only significantly reduces the action space considered by the policy but also alleviates the need to estimate the underlying data distribution. Extensive experiments on synthetic and real-world datasets show that TAFA outperforms the existing state-of-the-art baselines while achieving lower overall acquisition cost and computation.
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