AFA-BANDIT：预算约束下可证明近似最优的在线多特征分类

中文摘要 AI 辅助

主动特征获取（AFA）是一类分类问题，其中智能体在预测每个样本的标签之前决定获取哪些代价高昂的特征。与批量AFA（在完全观测数据上离线训练固定策略和分类器）不同，在线AFA在样本到达时根据揭示的标签更新其预测器。现有的在线方法要么使用深度强化学习（RL）而没有性能保证，要么最大化成本调整后的奖励而非强制执行全局预算。我们将在线AFA表述为组合背包老虎机（BwK）问题，该问题耦合了获取和预测。与先前基于老虎机的AFA和经典BwK不同，我们的设置具有组合复杂性、演化奖励、全局预算和结构化辅助信息。在该框架中，我们获得了优于标准BwK界限的改进遗憾上界，利用了基数感知置信界和子集更新结构。为避免指数级大的动作空间，我们提出了LP-Chain，一种变体，它搜索成本感知的特征子集链，其大小随特征数量线性增长。虽然遗憾上界特定于组合框架，但LP-Chain在经验上实现了可比的预测性能。在合成数据上，LP-Chain优于基于HEDGE的BwK和基于深度RL的在线AFA基线，并且能很好地扩展到更多特征。

英文摘要

Active Feature Acquisition (AFA) is a classification problem in which an agent decides which costly features to acquire before predicting each sample's label. Unlike batch AFA, which trains a fixed policy and classifier offline on fully observed data, online AFA updates its predictor from revealed labels as samples arrive. Existing online methods either use deep reinforcement learning (RL) without performance guarantees or maximize cost-adjusted reward rather than enforce a global budget. We formulate online AFA as a combinatorial Bandits with Knapsacks (BwK) problem that couples acquisition and prediction. Unlike prior bandit-based AFA and classical BwK, our setting has combinatorial complexity, evolving rewards, a global budget, and structured side information. We obtain an improved regret upper bound over standard BwK bounds in this framework, leveraging a cardinality-aware confidence bound and the subset update structure. To avoid an exponentially large action space, we propose \emph{LP-Chain}, a variant that searches a cost-aware chain of feature subsets with a size that grows linearly with the number of features. While the regret upper bound is specific to the combinatorial framework, \emph{LP-Chain} empirically achieves comparable predictive performance. On synthetic data, \emph{LP-Chain} outperforms HEDGE-based BwK and deep RL-based online AFA baselines and scales favorably to more features.

发表机构

• The University of Sydney（悉尼大学）

机构由 AI 辅助整理，请以论文原文为准。

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10 pages, 3 figures