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Hyperparameter optimization of non-linear machine learning models using bi-level data-driven optimization
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DOI：10.1016/j.compchemeng.2026.109671.png)
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Abstract
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• Hyperparameter tuning of machine learning models is addressed using data-driven optimization. • Performance of 17 different data-driven optimization algorithms over 6 case studies is quantified. • The data-driven bi-level approach outperforms grid search, random search, and Bayesian optimization. • Results show enhanced predictive performance on the testing sets for regression and classification problems.
Keywords:
hyperparameter optimization
data-driven optimization
bi-level approach
machine learning models
predictive performance
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3.9
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283
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