mlsanityData to 14 Oct 2026

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

arXiv 2306.00153 ↗First posted 31 May 2023cs.LG cs.AI cs.SCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 87 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

22 of 87 references.

References · 22

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10 more
  1. 2019Bayesian Symbolic Regressioncited by 78
  2. 2018How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanationcited by 72
  3. 2022Symbolic Regression is NP-hardcited by 71
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  6. 2019Epsilon-Lexicase Selection for Regressioncited by 31
  7. 2021An Approach to Symbolic Regression Using Feyncited by 20
  8. 2021Symbolic regression outperforms other models for small data setscited by 12
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  10. 2014GPTIPS 2: an open-source software platform for symbolic data miningcited by 3

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Studycited by 0