计算机与数字工程2024，Vol.52Issue(6)：1697-1702,6.DOI:10.3969/j.issn.1672-9722.2024.06.018
基于贝叶斯优化LightGBM的个人信用评估模型
Personal Credit Evaluation Model Based on Bayesian Optimization of LightGBM
摘要
Abstract
Aiming at the problems that traditional credit evaluation models cannot handle large-scale imbalanced data,train-ing time,and inaccurate evaluations,an optimized personal credit evaluation model is proposed.The model is based on the gradient boosting framework LightGBM,combined with the Bayesian global optimization algorithm for personal credit evaluation.In order to verify the applicability of the model,the Lending Club public data set is used to conduct related experiments and compared with the prediction results of logistic regression,random forest,and XGBoost models.The experimental results show that the personal credit evaluation effect of this model is better,the evaluation accuracy rate reaches 99.97%,and the F1-score of minority samples reaches 89.02%.关键词
个人信用评估/集成学习/LightGBM/超参数优化/特征重要度Key words
personal credit evaluation/integrated learning/LightGBM/hyperparameter optimization/importance of fea-tures分类
信息技术与安全科学引用本文复制引用
刘伯圣,邢进生..基于贝叶斯优化LightGBM的个人信用评估模型[J].计算机与数字工程,2024,52(6):1697-1702,6.基金项目
山西省软科学基金项目(编号:2011041033-03)资助. （编号:2011041033-03）
