Abstract #

The Machine Learning (ML) based solutions in manufacturing industrial contexts often require skilled resources. More practical non-expert software solutions are then desired to enhance the usability of ML algorithms. The algorithm selection and configuration is one of the most difficult tasks for users like manufacturing specialists. The identification of the most appropriate algorithm in an automatic manner is among the major research challenges to achieve optimal performance of ML tools. In this paper, we present an auto-explained Automated Machine Learning tool for Big Industrial Data (AMLBID) to better cope with the prominent challenges posed by the evolution of Big Industrial Data. It is a meta-learning based decision support system for the automated selection and tuning of implied hyperparameters for ML algorithms. Moreover, the framework is equipped with an explainer module that makes the outcomes transparent and interpretable for well-performing ML systems.

Citation #

@article{Garouani2022,
  doi = {10.1016/j.softx.2021.100919},
  url = {https://doi.org/10.1016/j.softx.2021.100919},
  year = {2022},
  month = jan,
  publisher = {Elsevier {BV}},
  volume = {17},
  pages = {100919},
  author = {Moncef Garouani and Adeel Ahmad and Mourad Bouneffa and Mohamed Hamlich},
  title = {{AMLBID}: An auto-explained Automated Machine Learning tool for Big Industrial Data},
  journal = {{SoftwareX}}
}