Automated learning of interpretable models with quantified uncertainty

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@Article{BOMARITO:2023:cma,

• author = "G. F. Bomarito and P. E. Leser and N. C. M. Strauss and K. M. Garbrecht and J. D. Hochhalter",
• title = "Automated learning of interpretable models with quantified uncertainty",
• journal = "Computer Methods in Applied Mechanics and Engineering",
• volume = "403",
• pages = "115732",
• year = "2023",
• ISSN = "0045-7825",
• DOI = "doi:10.1016/j.cma.2022.115732",
• URL = "https://www.sciencedirect.com/science/article/pii/S0045782522006879",
• keywords = "genetic algorithms, genetic programming, Interpretable machine learning, Symbolic regression, Bayesian model selection, Fractional Bayes factor",
• abstract = "Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently interpretable machine learning, but relatively little work has focused on the use of symbolic regression on noisy data and the accompanying necessity to quantify uncertainty. A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is introduced that uses model evidence (i.e., marginal likelihood) to formulate replacement probability during the selection phase of evolution. Model parameter uncertainty is automatically quantified, enabling probabilistic predictions with each equation produced by the GPSR algorithm. Model evidence is also quantified in this process, and its use is shown to increase interpretability, improve robustness to noise, and reduce overfitting when compared to a conventional GPSR implementation on both numerical and physical experiments",

}

Genetic Programming entries for Geoffrey F Bomarito Patrick E Leser Nolan Craig McGee Strauss Karl Michael Garbrecht Jacob Dean Hochhalter

Citations