We develop novel methodology for active feature acquisition (AFA), the study of how to sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes acquisition costs whilst still yielding accurate predictions. The AFA framework can be useful in a myriad of domains, including health care applications where the cost of acquiring additional features for a patient (in terms of time, money, risk, etc.) can be weighed against the expected improvement to diagnostic performance. Previous approaches for AFA have employed either: deep learning RL techniques, which have difficulty training policies in the AFA MDP due to sparse rewards and a complicated action space; deep learning surrogate generative models, which require modeling complicated multidimensional conditional distributions; or greedy policies, which fail to account for how joint feature acquisitions can be informative together for better predictions. In this work we show that we can bypass many of these challenges with a novel, nonparametric oracle based approach, which we coin the acquisition conditioned oracle (ACO). Extensive experiments show the superiority of the ACO to state-of-the-art AFA methods when acquiring features for both predictions and general decision-making.

翻译：我们提出了一种用于主动特征获取（AFA）的新方法论，旨在研究如何按顺序获取动态（基于每个实例）的特征子集，以最小化获取成本的同时仍能产生准确的预测。AFA框架在众多领域具有应用价值，包括医疗保健领域：在评估患者特征获取成本（如时间、金钱、风险等）与预期诊断性能提升之间的权衡时。以往的AFA方法主要采用以下三种策略：深度强化学习技术，由于稀疏奖励和复杂动作空间，在AFA马尔可夫决策过程中难以训练策略；深度代理生成模型，需要建模复杂的多维条件分布；或贪婪策略，未能考虑联合特征获取如何协同提供信息以实现更好的预测。在本研究中，我们展示了一种新颖的、基于非参数大语言模型的方法如何绕过这些挑战，我们将其命名为"获取条件大语言模型"（ACO）。大量实验表明，在获取用于预测和一般决策的特征时，ACO相比现有最先进的AFA方法具有显著优越性。