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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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Springer
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/d76eb77c3a604a2a8d809187d47ebc3c |
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| Sumario: | 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 tree and to improve generalization ability of the prediction model. |
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