Cited by 7Machine Learning and Data ClassificationAdvanced Neural Network ApplicationsMachine Fault Diagnosis Techniques

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

Open access
Solís-Martín, David, Galán-Páez, Juan, Borrego-Díaz, Joaquín·2025·Mathematics

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.

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Crossref · 10.3390/math13040555

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58 of 58 references verified0 citation errors · 0 not checkable

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  • Recent external literature emphasizes that models incorporating time-dependent parameters and learning curves provide a more accurate projection of long-term cost-effectiveness.

    “Recent external literature emphasizes that models incorporating time-dependent parameters and learning curves provide a more accurate projection of long-term cost-effectiveness39.”

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Authors
Solís-Martín, D., Galán-Páez, J., & Borrego-Díaz, J.
Affiliations
Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain
Venue
Mathematics
Publisher
MDPI AG
Published
2025-02-07
Volume
13
Issue
4
Pages
555
ISSN
2227-7390 (ISSN-L)
OpenAlex
W4407412014
Cited by
7
References
59
Citations by year
2026: 2 · 2025: 5
FWCI
2.83
Type
journal article
Language
en
Open access
Locations
  • Mathematics (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Agencia Estatal de Investigación (PID2023-147198NB-I00, DIN2019-010887/AEI/10.13039/50110001103)
  • FEDER (PID2023-147198NB-I00, DIN2019-010887/AEI/10.13039/50110001103)
  • UE (PID2023-147198NB-I00, DIN2019-010887/AEI/10.13039/50110001103)
  • Ministry of Science and Education of Spain (PID2023-147198NB-I00, DIN2019-010887/AEI/10.13039/50110001103)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Keywords
neural architecture search, early stopping, computational efficiency, predictive maintenance, industrial monitoring, automated machine learning, transfer learning

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