Abstract #

In context of the fourth industrial revolution (industry 4.0), the industrial big data is subject to grow rapidly to respond the agile industrial computing and manufacturing technologies. This data evolution can be captured using ubiquitous integrated sensors and multiple smart machines. We believe the use of data science methodologies, for the selection of models and configuration of hyper-parameters, may help to better control such data evolution. But, at the same time, the industrial practitioners and researchers often lack machine-learning expertise to directly retrieve the benefit from valuable manufacturing big data. Such a lack poses the major obstacle to yield value from even-though familiar data. In this case, a collaboration with data scientists may become an exigence along with the extensive machine learning knowledge which presumably may result to pursue further delays and effort. Multiple approaches for automating machine learning (AutoML) have been proposed for the past recent years in order to alleviate this deficiency. These approaches are expected to perform better along with accomplishment of computing resources which are mostly not readily accessible. To address this research challenge, in this paper, we propose a meta-learning based approach that may serve an effective decision support system for the AutoML process.

Citation #

@inproceedings{Garouani2021,
  doi = {10.5220/0010457107090716},
  url = {https://doi.org/10.5220/0010457107090716},
  year = {2021},
  publisher = {{SCITEPRESS} - Science and Technology Publications},
  author = {Moncef Garouani and Adeel Ahmad and Mourad Bouneffa and Arnaud Lewandowski and Gregory Bourguin and Mohamed Hamlich},
  title = {Towards the Automation of Industrial Data Science: A Meta-learning based Approach},
  booktitle = {Proceedings of the 23rd International Conference on Enterprise Information Systems}
}
}