用随机编码动态选特征，比传统方法更准更稳。
Stochastic Encodings for Active Feature Acquisition
- 构建随机潜在空间，通过多实现实例推理选特征
- 在多个真实与合成数据集上显著优于基线方法
- 适合需要高效获取关键特征的场景
主动特征获取是一个逐实例、序列决策问题，目标是根据当前观测动态选择测量哪个特征，每个测试实例独立处理。现有方法要么使用强化学习，训练困难；要么贪心最大化标签与未观测特征的条件互信息，导致短视决策。为此，我们提出一种监督训练的隐变量模型，通过在随机潜在空间中对大量未观测实现进行推理来做出采集决策。在广泛的真实与合成数据集上的大量实验表明，该方法稳定超越多种基线方法。
原文摘要 · Abstract (English)
Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.
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