AI 中文总结
本研究提出SynAFA策略，利用部分信息分解区分特征交互的冗余、独特与协同信息，实验表明协同信息在低预算下有益，但优势随预算增加而减弱，且难以转化为有效获取目标。
AI 中文摘要
主动特征获取（AFA）在预算约束下顺序选择信息丰富的特征。然而，现有策略很少区分交互特征所贡献的信息是冗余的、独特的还是协同的。我们提出了SynAFA，一种状态相关的AFA策略，它结合了成对联合信息和条件信息，并使用部分信息分解（PID）来表征其信息结构。在五个表格数据集和MNIST-loop上，性能表现各异。SynAFA在PhysioNet上的低预算设置中展现出最强的增益，其中协同和冗余特征对得到置换检验的支持，但其优势随着预算增加而减弱，并且不依赖于特征对提议。SynAFA在MiniBooNE上的表现显著差于几乎所有基线，在MNIST-loop上差于CAE。受控合成实验进一步表明，在预算内，当联合信息更以协同为主导时，SynAFA的优势上升，包括在总联合信息近似恒定的情况下。进一步分析表明，预算相关的性能衰减并未通过非贪婪局部搜索解决，后者改善了诊断性的集合级目标，但预测性能没有提升，通常显著更差，并且该目标的改进与固定分类器的预测效用弱对齐。这些发现刻画了成对协同何时能有益于AFA，同时揭示了将局部信息转化为有效获取目标的持续挑战。
英文摘要
Active feature acquisition (AFA) sequentially selects informative features under budget constraints. However, existing policies rarely distinguish whether information contributed by interacting features is redundant, unique, or synergistic. We introduce SynAFA, a state-dependent AFA policy that combines pairwise joint information and conditional information, with Partial Information Decomposition (PID) characterizing its information structure. Across five tabular datasets and MNIST-loop, performance is heterogeneous. SynAFA shows its strongest gains at low budgets on PhysioNet, where synergistic and redundant feature pairs are supported by permutation tests, but its advantage diminishes as budgets increase and does not depend on pair proposals. SynAFA performs significantly worse than nearly all baselines on MiniBooNE, and than CAE on MNIST-loop. Controlled synthetic experiments further show that, within budget, SynAFA's advantage rises as joint information becomes more synergy-dominated, including when total joint information is held approximately constant. Further analyses show that the budget-dependent erosion is not resolved by non-greedy local search, which improves a diagnostic set-level objective but leaves predictive performance no better, often significantly worse, and that improvements in this objective are weakly aligned with the fixed classifier's predictive utility. These findings characterize when pairwise synergy can benefit AFA while exposing a persistent challenge in translating local information into effective acquisition objectives.
