基于贝叶斯优化极端梯度提升树的电缆状态分类研究

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中图分类号：TP183 文献标志码：A DOI：10.13705/j. issn.1671-6841.2024079

文章编号：1671-6841（2025）06-0001-07

Abstract：Addressing the issue of low accuracy in cable condition classification due to imbalanced sample classes in multiclass classification problems，a cable condition classification method based on Bayesian-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 hyperparameter configuration. Then，this optimal hyperparameter configuration was applied to the XGBoost algorithm，which resulted in the Bo-XGBoost classification model.Finally，the verification through case studies demonstrated that this classfication method achieved higher accuracy compared to methods such as SVM，TabNet，and LightGBM， thereby providing a new direction for cable condition classification.

Key words： Bayesian optimization；extreme gradient boosting tree；cable condition classification ; hyperparameter optimization

0 引言

电缆的数量在电力现代化进程中迅速增长，形成了一个庞大且复杂的网络体系。（剩余11736字）