论文标题
在了解具有功能归因算法的可控因素的影响时：医学案例研究
On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study
论文作者
论文摘要
功能归因XAI算法使他们的用户能够通过其功能重要性计算来深入了解大数据集的基本模式。现有的功能归因算法同质地处理数据集中的所有功能，这可能导致误解变化特征值的后果。在这项工作中，我们考虑将功能分配到可控和不可控制的零件中，并提出可控因素特征归因（CAFA）方法，以计算可控特征的相对重要性。我们进行了将CAFA应用于两个现有数据集的实验，以及我们自己的Covid-19非药物控制措施数据集。实验结果表明，使用CAFA，我们能够在解释中排除无法控制的功能的影响，同时将完整的数据集保留为预测。
Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction.
