Towards Efficient and Explainable Automated Machine Learning Pipelines Design

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作者

Moncef Garouani¹ , Mourad Bouneffa¹ , Adeel Ahmad¹

1. Univ. Littoral Côte d’Opale

会议/活动

International Conference on Artificial Intelligence and Big Data in Digital Era (ICABDE), December 2021 (虚拟会议)

海报摘要

The Machine Learning (ML) based data analytics solutions often require skilled resources. More practical non-expert software solutions are then desired to enhance the usability of ML algorithms. The algorithms selection and configuration is one of the most difficult tasks for non-experts users. 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 poster, we present an auto-explained Automated Machine Learning tool 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.

关键词

Automated machine learning, Meta-learning, Explainable AutoML, Decision-support systems

研究领域

Computer and Information Science

参考文献

1. Garouani, M., Ahmad, A., Bouneffa., et al . Using meta learning for automated algorithms selection and configuration: an experimental framework for big industrial data. Journal of Big Data 9, 57 (2022). https://doi.org/10.1186/s40537-022-00612-4.
2. Garouani, M., Ahmad, A., Bouneffa, M, et al . Towards big industrial data mining through explainable automated machine learning. The International Journal of Advanced Manufacturing Technology (2022). https://doi.org/10.1007/s00170-022-08761-9.
3. Garouani, M., Ahmad, A., Bouneffa, M., et al AMLBID: An Automated Machine Learning tool for Big Industrial Data. SoftwareX (2021) 100919, https://doi.org/10.1016/j.softx.2021.100919.

基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

None declared.

数据可用性声明

The datasets generated during and/or analyzed during the current study are available from the corresponding author author on reasonable request.

知识共享许可协议

Copyright © 2022 Garouani et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.