A Model for Learning-Curve Estimation in Efficient Neural Architecture Search and Its Application in Predictive Health Maintenance

David Solís-Martín, Juan Galán-Páez and Joaquín Borrego-Díaz ()
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David Solís-Martín: Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain
Juan Galán-Páez: Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain
Joaquín Borrego-Díaz: Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain

Mathematics, 2025, vol. 13, issue 4, 1-37

Abstract: A persistent challenge in machine learning is the computational inefficiency of neural architecture search (NAS), particularly in resource-constrained domains like predictive maintenance. This work introduces a novel learning-curve estimation framework that reduces NAS computational costs by over 50% while maintaining model performance, addressing a critical bottleneck in automated machine learning design. By developing a data-driven estimator trained on 62 different predictive maintenance datasets, we demonstrate a generalized approach to early-stopping trials during neural network optimization. Our methodology not only reduces computational resources but also provides a transferable technique for efficient neural network architecture exploration across complex industrial monitoring tasks. The proposed approach achieves a remarkable balance between computational efficiency and model performance, with only a 2% performance degradation, showcasing a significant advancement in automated neural architecture optimization strategies.

Keywords: learning curves; neural architecture search; predictive maintenance; Bayesian optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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