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

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

Abstract 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 ma...

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Bibliographic Details
Main Authors: Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, Mohamed Hamlich, Gregory Bourguin, Arnaud Lewandowski
Format: Article
Language:English
Published: SpringerOpen 2022-04-01
Series:Journal of Big Data
Subjects:
Algorithms selection
Machine learning
AutoML
Meta-learning
Industry 4.0
Big industrial data
Online Access:https://doi.org/10.1186/s40537-022-00612-4

Internet

https://doi.org/10.1186/s40537-022-00612-4

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