An ensemble credit scoring model based on logistic regression with heterogeneous balancing and weighting effects
作者：
Highlights：
• Propose logistic-BWE model with heterogeneous balancing and weighting effects.
• Build sub models on data sets with different imbalance ratios.
• A dynamic weighting method is proposed to enhance the flexibility and accuracy.
• The model can improve the prediction performance especially for default samples.
• The model is robustness while maintaining the interpretability of results.
摘要
•Propose logistic-BWE model with heterogeneous balancing and weighting effects.•Build sub models on data sets with different imbalance ratios.•A dynamic weighting method is proposed to enhance the flexibility and accuracy.•The model can improve the prediction performance especially for default samples.•The model is robustness while maintaining the interpretability of results.
论文关键词：Logistic regression,Logistic-BWE model,Sample balancing algorithm,Ensemble credit scoring models,Dynamic weighting
论文评审过程：Received 14 July 2022, Revised 25 August 2022, Accepted 28 August 2022, Available online 5 September 2022, Version of Record 7 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118732
