Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data

Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such “industrial big data” can relay its usefulness to reach further utilitarian applications. In this context, Machine Learning (ML) is among the major predi...

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Veröffentlicht in:	Journal of big data Jg. 9; H. 1; S. 1 - 20
Hauptverfasser:	Garouani, Moncef, Ahmad, Adeel, Bouneffa, Mourad, Hamlich, Mohamed, Bourguin, Gregory, Lewandowski, Arnaud
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 29.04.2022 Springer Nature B.V Springer SpringerOpen
Schlagworte:	Algorithms Algorithms selection Automation AutoML Bayesian analysis Big Data Big industrial data Communications Engineering Computational Science and Engineering Computer Science Data analysis Data mining Data Mining and Knowledge Discovery Data quality Database Management Empirical analysis Evolutionary algorithms Formalism Impact analysis Industry 4.0 Information retrieval Information Storage and Retrieval Machine learning Manufacturing Mathematical Applications in Computer Science Meta-learning Networks Optimization Prediction models Predictions Reinforcement Specification Task complexity Usefulness Decision support systems Big industrial data Algorithms selection Meta-learning Machine learning Industry 4.0 AutoML
ISSN:	2196-1115, 2196-1115
Online-Zugang:	Volltext
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