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Towards big industrial data mining through explainable automated machine learning

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Abstract

Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence (AI) models, but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameters configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field.

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Availability of data and materials

All data generated or analyzed during this study are included in this paper.

Code availability

Software code is included in the study github repository: https://github.com/LeMGarouani/AMLBID.

Notes

  1. https://archive.ics.uci.edu/

  2. https://www.openml.org/

  3. https://www.kaggle.com/

  4. https://sci2s.ugr.es/keel/

  5. https://github.com/LeMGarouani/AMLBID

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Acknowledgements

The authors thank the Université du Littoral Côte d’Opale (ULCO), France, School of engineering’s and business’ sciences and technics (HESTIM), Morocco and CNRST Morocco for the partial financial support, and all the participants involved in the system evaluation for their constructive discussions and valuable suggestions.

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Authors and Affiliations

  1. Univ. Littoral Cote d’Opale, UR 4491, LISIC, Laboratoire d’Informatique Signal et Image de la Cote d’Opale, F-62100, Calais, France

    Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, Gregory Bourguin & Arnaud Lewandowski

  2. CCPS Laboratory, ENSAM, University of Hassan II, Casablanca, Morocco

    Moncef Garouani & Mohamed Hamlich

  3. Study and Research Center for Engineering and Management (CERIM), HESTIM, Casablanca, Morocco

    Moncef Garouani

Authors
  1. Moncef Garouani
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  2. Adeel Ahmad
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  3. Mourad Bouneffa
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  4. Mohamed Hamlich
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Correspondence to Moncef Garouani.

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Appendix

Appendix

See Tables 11, 12, 13, 14, 15, 16, and 17 and Fig. 12

Table 11 SVM hyperparameters tuned in the experiments
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Table 12 Random Forest & Extra Trees Hyperparameters tuned in the experiments
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Table 13 Adaboost Hyperparameters tuned in the experiments
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Table 14 Decision Trees Hyperparameters tuned in the experiments
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Table 15 Logistic Regression Hyperparameters tuned in the experiments
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Table 16 SGD Classifier Hyperparameters tuned in the experiments
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Table 17 Gradient Boosting Hyperparameters tuned in the experiments
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Fig. 12
figure 12

The Post-Study System Usability Questionnaire

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Garouani, M., Ahmad, A., Bouneffa, M. et al. Towards big industrial data mining through explainable automated machine learning. Int J Adv Manuf Technol 120, 1169–1188 (2022). https://doi.org/10.1007/s00170-022-08761-9

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