Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
作者：
Highlights：
• Development of the innovative machine learning based design support system (DesSS).
• Comparison with other state-of-the-art machine learning and deep learning model.
• Validation of the DesSS on two real use case datasets.
• High computational speed and accuracy compared to simulation tools.
• Trade-off between the model interpretability, computation effort and accuracy.
摘要
•Development of the innovative machine learning based design support system (DesSS).•Comparison with other state-of-the-art machine learning and deep learning model.•Validation of the DesSS on two real use case datasets.•High computational speed and accuracy compared to simulation tools.•Trade-off between the model interpretability, computation effort and accuracy.
论文关键词：Design support system,Machine learning,Decision tree,Nearest-Neighbor,Neighborhood component features selection
论文评审过程：Received 8 February 2019, Revised 2 August 2019, Accepted 9 August 2019, Available online 10 August 2019, Version of Record 21 August 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112869
