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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Weiping Xu, Wendi Li, Yao Zhang, Taihua Zhang, Huawei Chen
Formato:	article
Lenguaje:	EN
Publicado:	Springer 2021
Materias:	Wear condition prediction XGBoost feature selection SMOTE Bagging-GBDT Science Q Technology T
Acceso en línea:	https://doaj.org/article/d76eb77c3a604a2a8d809187d47ebc3c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d76eb77c3a604a2a8d809187d47ebc3c
record_format	dspace
spelling	oai:doaj.org-article:d76eb77c3a604a2a8d809187d47ebc3c2021-11-21T12:12:34ZBagging-gradient boosting decision tree based milling cutter wear status prediction modelling10.1007/s42452-021-04856-22523-39632523-3971https://doaj.org/article/d76eb77c3a604a2a8d809187d47ebc3c2021-11-01T00:00:00Zhttps://doi.org/10.1007/s42452-021-04856-2https://doaj.org/toc/2523-3963https://doaj.org/toc/2523-3971Article 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.Weiping XuWendi LiYao ZhangTaihua ZhangHuawei ChenSpringerarticleWear condition predictionXGBoost feature selectionSMOTEBagging-GBDTScienceQTechnologyTENSN Applied Sciences, Vol 3, Iss 12, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wear condition prediction XGBoost feature selection SMOTE Bagging-GBDT Science Q Technology T
spellingShingle	Wear condition prediction XGBoost feature selection SMOTE Bagging-GBDT Science Q Technology T Weiping Xu Wendi Li Yao Zhang Taihua Zhang Huawei Chen Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
description	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.
format	article
author	Weiping Xu Wendi Li Yao Zhang Taihua Zhang Huawei Chen
author_facet	Weiping Xu Wendi Li Yao Zhang Taihua Zhang Huawei Chen
author_sort	Weiping Xu
title	Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
title_short	Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
title_full	Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
title_fullStr	Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
title_full_unstemmed	Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
title_sort	bagging-gradient boosting decision tree based milling cutter wear status prediction modelling
publisher	Springer
publishDate	2021
url	https://doaj.org/article/d76eb77c3a604a2a8d809187d47ebc3c
work_keys_str_mv	AT weipingxu bagginggradientboostingdecisiontreebasedmillingcutterwearstatuspredictionmodelling AT wendili bagginggradientboostingdecisiontreebasedmillingcutterwearstatuspredictionmodelling AT yaozhang bagginggradientboostingdecisiontreebasedmillingcutterwearstatuspredictionmodelling AT taihuazhang bagginggradientboostingdecisiontreebasedmillingcutterwearstatuspredictionmodelling AT huaweichen bagginggradientboostingdecisiontreebasedmillingcutterwearstatuspredictionmodelling
_version_	1718419153426055168

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

Ejemplares similares