Active feature acquisition (AFA) agents, crucial in domains like healthcare
where acquiring features is often costly or harmful, determine the optimal set
of features for a subsequent classification task. As deploying an AFA agent
introduces a shift in missingness distribution, it's vital to assess its
expected performance at deployment using retrospective data. In a companion
paper, we introduce a semi-offline reinforcement learning (RL) framework for
active feature acquisition performance evaluation (AFAPE) where features are
assumed to be time-dependent. Here, we study and extend the AFAPE problem to
cover static feature settings, where features are time-invariant, and hence
provide more flexibility to the AFA agents in deciding the order of the
acquisitions. In this static feature setting, we derive and adapt new inverse
probability weighting (IPW), direct method (DM), and double reinforcement
learning (DRL) estimators within the semi-offline RL framework. These
estimators can be applied when the missingness in the retrospective dataset
follows a missing-at-random (MAR) pattern. They also can be applied to
missing-not-at-random (MNAR) patterns in conjunction with appropriate existing
missing data techniques. We illustrate the improved data efficiency offered by
the semi-offline RL estimators in synthetic and real-world data experiments
under synthetic MAR and MNAR missingness.

在这篇研究论文中，我们介绍了一种半离线强化学习框架，用于评估部署活动特征获取性能 (active feature acquisition performance evaluation, AFAPE)。首先，我们将 AFAPE 问题扩展到静态特征环境中，从而为 AFA 代理提供决策特征获取顺序的更大灵活性。然后，在半离线强化学习框架中，我们推导和适应了新的逆概率权重估计器 (inverse probability weighting, IPW)，直接方法估计器 (direct method, DM) 和双重强化学习估计器 (double reinforcement learning, DRL)。这些估计器可应用于具有缺失数据的回顾数据集，无论缺失性是否遵循随机缺失 (missing-at-random, MAR) 模式或非随机缺失 (missing-not-at-random, MNAR) 模式。在合成数据和真实世界数据的实验中，我们展示了半离线强化学习估计器在合成 MAR 和 MNAR 缺失数据下的改进数据效率。

静态特征设置的主动特征获取方法评估

Evaluation of Active Feature Acquisition Methods for Static Feature  Settings

This paper introduces a novel approach to active feature acquisition for
classification, which is the task of sequentially selecting the most
informative subset of features to achieve optimal prediction performance during
testing while minimizing cost. The proposed approach involves a new lazy model
that is significantly faster and more efficient compared to existing methods,
while still producing comparable accuracy results. During the test phase, the
proposed approach utilizes Fisher scores for feature ranking to identify the
most important feature at each step. In the next step the training dataset is
filtered based on the observed value of the selected feature and then we
continue this process to reach to acceptable accuracy or limit of the budget
for feature acquisition. The performance of the proposed approach was evaluated
on synthetic and real datasets, including our new synthetic dataset, CUBE
dataset and also real dataset Forest. The experimental results demonstrate that
our approach achieves competitive accuracy results compared to existing
methods, while significantly outperforming them in terms of speed. The source
code of the algorithm is released at github with this link:
this https URL

本文提出了一种新的主动特征获取方法，使用 Lazy Model 和 Fisher scores 确定特征子集以在测试期间最大程度地提高预测性能同时最小化成本（即特征获取成本达到可接受的精度或预算限制），并在合成和实际数据集上进行了实验来证明其准确性和速度优势。

测试阶段顺序特征选择的快速分类

Fast Classification with Sequential Feature Selection in Test Phase

We consider the problem of active feature acquisition, where we sequentially
select the subset of features in order to achieve the maximum prediction
performance in the most cost-effective way. In this work, we formulate this
active feature acquisition problem as a reinforcement learning problem, and
provide a novel framework for jointly learning both the RL agent and the
classifier (environment). We also introduce a more systematic way of encoding
subsets of features that can properly handle innate challenge with missing
entries in active feature acquisition problems, that uses the orderless
LSTM-based set encoding mechanism that readily fits in the joint learning
framework. We evaluate our model on a carefully designed synthetic dataset for
the active feature acquisition as well as several real datasets such as
electric health record (EHR) datasets, on which it outperforms all baselines in
terms of prediction performance as well feature acquisition cost.

提供了一个新的框架，通过强化学习问题对活动特征获取问题进行建模，并引入了一种更系统完善的特征子集编码机制，该方法使用无序 LSTM-based 的集合编码机制来进行学习，从而在包含有缺失条目的情况下得到了很好的应用，并在活动特征获取方面性能均优于其他基线系统。