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  • https://doi.org/10.1109/nmitcon65824.2025.11188213Copy DOI Icon

Meta-Learning Enhanced AutoML for Multi-Objective Optimization in Small and Medium-Sized Enterprises

  • Aug 1, 2025
  • Mohammed I Habelalmateen +4 more
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Abstract

In recent years, the quick deployment of Machine Learning in Small and Medium-sized Enterprises (SMEs) has been delayed by the complexity of model design and the lack of skilled personnel. However, traditional Automated Machine Learning (AutoML) models often restart learning from scratch for each task, resulting in high computational costs and delayed optimization. Therefore, to address these issues, a meta-learning-enhanced AutoML with Preference-Based Optimization (MetaAutoMPO) framework was proposed. Initially, it integrates a meta-learning layer that extracts taskspecific meta-features and retrieves optimal model configurations from similar tasks before starting the training process. Subsequently, regression models were constructed using a Multilayer Perceptron (MLP) with hyperparameters tuned via Bayesian optimization. The learned models are then used as objective functions in a reference-point-based Nondominated Sorting Genetic Algorithm II (NSGA-II) to generate preference-aware process parameterizations. Finally, experimental validation demonstrates that MetaAutoMPO achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 5 \%}$</tex> faster convergence and improves productivity (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\uparrow 3.19 \%$</tex>) while reducing defect rates (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\downarrow$</tex> 2.15%) compared with conventional AutoML methods.

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