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Related Experiment Videos

Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.

Amir Shahbazi1,2, Hasan Nikkhah1,2, Zahir Aghayev1,2

  • 1Department of Chemical & Biomolecular Engineering, University of Connecticut, , Storrs, 06269, CT, USA.

Computers & Chemical Engineering
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bi-level data-driven optimization approach for machine learning (ML) hyperparameter tuning. This method enhances model accuracy and generalization compared to traditional techniques.

Keywords:
Cross-validationData-driven OptimizationHyperparameter TuningMachine Learning

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Optimization
  • Computational Science

Background:

  • Hyperparameter tuning is crucial for machine learning (ML) model performance but often relies on inefficient trial-and-error methods.
  • Conventional techniques like grid search, random search, and Bayesian optimization can be exhaustive and computationally expensive.
  • Existing methods struggle to capture the complex interdependence between hyperparameter selection and model evaluation.

Purpose of the Study:

  • To present a systematic and data-driven decision-making approach for tuning ML model hyperparameters using cross-validation.
  • To offer a more efficient and precise alternative to conventional hyperparameter optimization methods.
  • To demonstrate the enhancement of bi-level data-driven optimization, particularly with non-linear loss functions.

Main Methods:

  • Formulating hyperparameter tuning as a bi-level optimization problem.
  • Utilizing the Data-driven Optimization of bi-level Mixed-Integer non-linear problems (DOMINO) framework to approximate the bi-level problem as a single-level problem.
  • Evaluating 17 diverse data-driven optimization algorithms (heuristic, deterministic, local, global, sample-based, model-based) on regression and classification tasks.

Main Results:

  • The data-driven bi-level approach significantly outperforms conventional tuning algorithms in predictive accuracy on unseen data.
  • ML models tuned with this method exhibit superior generalization capabilities across various tasks.
  • Local optimization algorithms within DOMINO are efficient for single hyperparameters, while global algorithms excel with multiple or non-linear hyperparameters.

Conclusions:

  • Bi-level data-driven optimization, implemented via the DOMINO framework, provides a powerful and efficient strategy for hyperparameter tuning.
  • This approach enhances the accuracy and generalization of machine learning models, especially in complex scenarios.
  • The choice between local and global optimization algorithms within the framework depends on the model's hyperparameter complexity and characteristics.