评估特征对分类问题的影响：在 COVID-19 患者中的应用
Assessment of the influence of features on a classification problem: an application to COVID-19 patients
AI总结：
提出一种基于合作博弈 Shapley 值的特征影响衡量方法并给出公理化刻画，通过实验验证后用于分析 COVID-19 患者人口统计学及风险因素对疾病演变事件的影响。
摘要：
本文探讨机器学习技术所处理的分类问题中的一个重要主题：评估每个特征对个体分类的影响。具体而言，利用合作博弈的 Shapley 值引入了一种衡量该影响的指标。此外，基于效率和平衡贡献的性质，对所提出的衡量指标给出了公理化刻画。进一步地，为验证该衡量指标的适当性能，设计了若干实验。最后，将所引入的方法应用于一个 COVID-19 患者样本，以研究某些人口统计学因素或风险因素对与疾病演变相关的各类关注事件的影响。
英文摘要：
This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
