Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling

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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Bibliographic Details
Main Authors: Weiping Xu, Wendi Li, Yao Zhang, Taihua Zhang, Huawei Chen
Format: Article
Language:English
Published: Springer 2021-11-01
Series:SN Applied Sciences
Subjects:
Wear condition prediction
XGBoost feature selection
SMOTE
Bagging-GBDT
Online Access:https://doi.org/10.1007/s42452-021-04856-2

Internet

https://doi.org/10.1007/s42452-021-04856-2

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