Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
Article Highlights Candidate parameter sets are extracted from multi-domain (time, frequency, and time–frequency). Topmost significant features are screened by XGBoost selection, and balanced via SMOTE technology. Bagging idea is introduced for parallel calculation of the gradient boosting decision...
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| Main Authors: | , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Springer
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/d76eb77c3a604a2a8d809187d47ebc3c |
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