A Model for Learning-Curve Estimation in Efficient Neural Architecture Search and Its Application in Predictive Health Maintenance
Open accessAbstract
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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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.”
From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as [39]
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Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare
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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
- gold
- doi.org
- Licence
- 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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