Information Fusion.

Information fusion via symbolic regression: A tutorial in the context of human health.

Jennifer J. Schnur, Nitesh V. Chawla. Information Fusion, Volume 92, Pages 326-335. (April 2023).

Highlights.

Existing approaches for symbolic regression are presented and discussed.
Model interpretability standards are explored in the context of symbolic modeling.
An application of symbolic regression is demonstrated in the human health domain.

Abstract.

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.

doi: https://doi.org/10.1016/j.inffus.2022.11.030

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