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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Published in:	Journal of big data Vol. 9; no. 1; pp. 1 - 20
Main Authors:	Garouani, Moncef, Ahmad, Adeel, Bouneffa, Mourad, Hamlich, Mohamed, Bourguin, Gregory, Lewandowski, Arnaud
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 29.04.2022 Springer Nature B.V Springer SpringerOpen
Subjects:	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
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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 major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes.
AbstractList	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 predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes. 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 predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes. 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 major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes.
ArticleNumber	57
Author	Ahmad, Adeel Garouani, Moncef Bouneffa, Mourad Hamlich, Mohamed Lewandowski, Arnaud Bourguin, Gregory
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Cites_doi	10.1109/4235.585893 10.1007/978-3-319-58967-1_5 10.1007/s10845-020-01712-9 10.1016/j.ejor.2006.12.004 10.1186/s40537-020-00340-7 10.1016/j.cam.2009.10.030 10.1186/s40537-019-0271-7 10.1109/tsm.2019.2941752 10.1016/j.engappai.2019.103289 10.1016/j.procir.2018.03.215 10.1002/cpe.4128 10.1007/s10845-020-01623-9 10.1080/21693277.2016.1192517 10.1007/978-3-030-05318-5_6 10.1007/s00170-022-08761-9 10.1007/978-1-4842-4470-8 10.1007/s10489-019-01516-2 10.1007/978-3-319-92901-9_6 10.1016/C2015-0-02071-8 10.1007/978-3-030-05318-5_4 10.1007/s10044-012-0280-z 10.1007/s10845-020-01667-x 10.1016/j.aei.2021.101253 10.1007/978-3-030-05318-5 10.1016/j.softx.2021.100919 10.1016/j.compind.2017.12.005 10.1007/s10845-013-0774-6 10.1016/j.aei.2021.101321 10.1186/s40537-021-00542-7 10.1186/s40537-019-0185-4 10.1016/j.aei.2019.01.007 10.1504/IJMR.2017.088399 10.1007/978-3-030-05318-5_8 10.1016/j.aei.2018.08.013 10.1007/978-3-030-69992-5_10 10.1007/s10845-019-01531-7 10.1016/j.strusafe.2020.102047 10.1145/2908812.2908918 10.1109/nfv-sdn53031.2021.9665051 10.1109/BigData.2017.8257923 10.1007/978-3-030-05318-5_10 10.5220/0010457107090716 10.1007/978-3-030-30000-5_42 10.1145/3269206.3269299 10.23919/SOFTCOM.2019.8903672
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Snippet	Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such “industrial... Abstract Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such...
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SubjectTerms	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
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Title	Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data
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