发表机构
Curtin Business School, Curtin University; School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（科廷大学科廷商学院; 科廷大学电气、计算与数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对主动特征获取中贪心方法短视、现有方法优化复杂的问题，提出基于Bellman目标反向拟合的监督式BRiG-AFA方法，在多个基准上验证了其非近视决策的有效性并明确了适用边界。
AI 中文摘要
主动特征获取（AFA）研究在预算约束下，针对每个测试样本下一步应测量哪个未观测特征。贪心规则易于训练，但会忽略那些仅能通过后续获取才能体现价值的上下文特征；而强化学习和生成式方法则会引入复杂的优化问题或条件密度估计难题。我们提出BRiG-AFA，这是一种可部署的监督式替代方案，它为每个剩余预算学习一个独立的、以候选特征为条件的风险-去向函数。该方法从单步终端分类风险出发，利用Bellman目标反向拟合这些函数；推理阶段仅使用观测值、掩码、候选特征标识和剩余预算，通过贪心方式最小化学到的终端风险。一项受控的非近视基准实验验证了预期的机制：在预算为2和3时，BRiG-AFA的准确率相比其单步消融模型分别提升了4.84±2.17和4.39±1.10个百分点（均值±标准误，基于5个随机种子）。在包含20个候选像素的Fashion-MNIST数据集上，该方法在所有已报告的非平凡预算下的平均准确率均有提升，其中在4次获取时提升了10.20±0.74个百分点；其在预算集{2,4,8,12,16}上的平均成对增益为3.50±0.37个百分点。在MiniBooNE数据集上的3种子研究中，该方法在小预算下表现不一，但在8次和16次获取时效果为正，这指出了当前方法的边界，而非支持普适性结论。这些结果为直接Bellman风险回归建立了可复现的机制层面论据，并明确了要达到最优水平对比仍需开展的实验。
英文摘要
Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but can overlook context features whose value is realized only through later acquisitions, while reinforcement-learning and generative approaches introduce difficult optimization or conditional-density estimation. We introduce \method, a deployable, supervised alternative that learns a separate candidate-conditioned risk-to-go function for every remaining budget. Starting from the one-step terminal classification risk, the functions are fitted backward with Bellman targets; inference greedily minimizes the learned terminal risk using only observed values, the mask, candidate identity, and remaining budget. A controlled non-myopic benchmark shows the expected mechanism: at budgets two and three, \method improves accuracy over its one-step ablation by $4.84\pm2.17$ and $4.39\pm1.10$ percentage points (mean $\pm$ standard error over five seeds). On Fashion-MNIST with 20 candidate pixels, it improves accuracy at every nontrivial reported budget on average, including $10.20\pm0.74$ points at four acquisitions; its mean paired gain across budgets $\{2,4,8,12,16\}$ is $3.50\pm0.37$ points. A three-seed MiniBooNE study is mixed at small budgets but positive at 8 and 16 acquisitions, identifying a current boundary rather than supporting a universal claim. These results establish a reproducible mechanism-level case for direct Bellman risk regression and delimit the experiments still needed for state-of-the-art comparison.
