Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we propose models that dynamically acquire new features to further improve the prediction assessment. To trade off the improvement with the cost of acquisition, we leverage an information theoretic metric, conditional mutual information, to select the most informative feature to acquire. We leverage a generative model, arbitrary conditional flow (ACFlow), to learn the arbitrary conditional distributions required for estimating the information metric. We also learn a Bayesian network to accelerate the acquisition process. Our model demonstrates superior performance over baselines evaluated in multiple settings.

翻译：许多现实世界局势允许在用有限或不确定的数据进行评估时获取更多的相关信息,然而,传统的ML方法要么要求事先获得所有特征,要么将其中一部分视为无法获取的缺失数据。在这项工作中,我们提出了动态获得新特征的模式,以进一步改进预测评估。为了将改进与获取成本相抵,我们利用信息理论衡量标准、有条件的相互信息,选择获取的信息最丰富的特征。我们利用一种基因化模型,即任意的有条件流动(ACFlow),学习估算信息计量标准所需的任意有条件分布。我们还学习了贝叶斯网络加快获取进程。我们的模型显示,相对于多个环境中评估的基线,我们的模型表现优于所评估的基线。