A novel XGBoost extension for credit scoring class-imbalanced data combining a generalized extreme value link and a modified focal loss function
作者：
Highlights：
• We propose several XGBoost extensions to learn class-imbalanced data.
• Fit the techniques to a vast, dynamic and public Freddie Mac mortgages data.
• Assess model performance relative to data complexity and external validity.
• Gauge the business value of the techniques using a profitability measure.
• A proposed GEV link with a modified focal loss in XGBoost is best for rare/outlier cases.
摘要
•We propose several XGBoost extensions to learn class-imbalanced data.•Fit the techniques to a vast, dynamic and public Freddie Mac mortgages data.•Assess model performance relative to data complexity and external validity.•Gauge the business value of the techniques using a profitability measure.•A proposed GEV link with a modified focal loss in XGBoost is best for rare/outlier cases.
论文关键词：Class imbalance,Machine learning,Credit scoring,XGBoost,Freddie Mac
论文评审过程：Received 20 March 2021, Revised 20 March 2022, Accepted 10 April 2022, Available online 13 April 2022, Version of Record 26 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117233
