标题： 通过符号回归的信息融合：在人类健康背景下的教程 显示英文标题

标题： Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health

摘要： 本文教程论文提供了符号回归（SR）的一般概述，特别关注可解释性标准。 我们认为，尽管在文献中其定义仍存在争议，可解释建模是一种支持评估成功信息融合的实用方法。 为了说明SR作为一种建模技术的优势，我们使用来自疾病控制与预防中心（CDC）的公开国家健康与营养检查调查（NHANES）数据，在健康与营养领域展示了一个应用，将人体测量标志物融合为一个简单的数学表达式来估计体脂百分比。 我们讨论了与SR建模相关的优点和挑战，并提供了所学模型的定性和定量分析。 显示英文摘要

摘要： This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its definition is still disputed in the literature, is a practical way to support the evaluation of successful information fusion. In order to convey the benefits of SR as a modeling technique, we demonstrate an application within the field of health and nutrition using publicly available National Health and Nutrition Examination Survey (NHANES) data from the Centers for Disease Control and Prevention (CDC), fusing together anthropometric markers into a simple mathematical expression to estimate body fat percentage. We discuss the advantages and challenges associated with SR modeling and provide qualitative and quantitative analyses of the learned models.