郑州大学学报（理学版）2025，Vol.57Issue(6)：1-7,7.DOI:10.13705/j.issn.1671-6841.2024079
基于贝叶斯优化极端梯度提升树的电缆状态分类研究
Research on Cable Condition Classification Based on Bayesian Optimization of Extreme Gradient Boosting Tree
摘要
Abstract
Addressing the issue of low accuracy in cable condition classification due to imbalanced sam-ple classes in multiclass classification problems,a cable condition classification method based on Bayes-ian-optimized extreme gradient boosting was proposed.Firstly,Bayesian optimization was employed to train the hyperparameters within the XGBoost algorithm,with the aim of acquiring the optimal hyperpa-rameter configuration.Then,this optimal hyperparameter configuration was applied to the XGBoost algo-rithm,which resulted in the Bo-XGBoost classification model.Finally,the verification through case stud-ies demonstrated that this classification method achieved higher accuracy compared to methods such as SVM,TabNet,and LightGBM,thereby providing a new direction for cable condition classification.关键词
贝叶斯优化/极端梯度提升树/电缆状态分类/超参数优化Key words
Bayesian optimization/extreme gradient boosting tree/cable condition classification/hyperparameter optimization分类
信息技术与安全科学引用本文复制引用
佘维,王欣,陈斌,吕钟毓,张海丽,田钊..基于贝叶斯优化极端梯度提升树的电缆状态分类研究[J].郑州大学学报（理学版）,2025,57(6):1-7,7.基金项目
嵩山实验室预研项目(YYYY022022003) （YYYY022022003）
河南省重点研发与推广专项(科技攻关)(212102310039) （科技攻关）
