In many real-world scenarios, acquiring all features of a data instance can be expensive or impractical due to monetary cost, latency, or privacy concerns. Active Feature Acquisition (AFA) addresses this challenge by dynamically selecting a subset of informative features for each data instance, trading predictive performance against acquisition cost. While numerous methods have been proposed for AFA, ranging from greedy information-theoretic strategies to non-myopic reinforcement learning approaches, fair and systematic evaluation of these methods has been hindered by the lack of standardized benchmarks. In this paper, we introduce AFABench, the first benchmark framework for AFA. Our benchmark includes a diverse set of synthetic and real-world datasets, supports a wide range of acquisition policies, and provides a modular design that enables easy integration of new methods and tasks. We implement and evaluate representative algorithms from all major categories, including static, greedy, and reinforcement learning-based approaches. To test the lookahead capabilities of AFA policies, we introduce a novel synthetic dataset, AFAContext, designed to expose the limitations of greedy selection. Our results highlight key trade-offs between different AFA strategies and provide actionable insights for future research. The benchmark code is available at: https://github.com/Linusaronsson/AFA-Benchmark.

翻译：在许多现实场景中，由于成本、延迟或隐私考虑，获取数据实例的所有特征可能代价高昂或不切实际。主动特征获取通过为每个数据实例动态选择信息丰富的特征子集来解决这一挑战，在预测性能与获取成本之间进行权衡。尽管已提出从贪婪信息论策略到非近视强化学习方法等多种AFA方法，但缺乏标准化基准阻碍了这些方法的公平系统评估。本文提出首个AFA基准框架AFABench。该基准包含多样化的合成与真实数据集，支持广泛的获取策略，并提供模块化设计以方便新方法与任务的集成。我们实现并评估了所有主要类别（包括静态、贪婪和基于强化学习的方法）的代表性算法。为测试AFA策略的前瞻能力，我们设计了新型合成数据集AFAContext，用于揭示贪婪选择的局限性。实验结果阐明了不同AFA策略间的关键权衡，并为未来研究提供了可行见解。基准代码发布于：https://github.com/Linusaronsson/AFA-Benchmark。