An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection
作者：
Highlights：
• An uncertainty-oriented credit scoring framework with multi-objective feature selection is developed to tackle the credit classification task under uncertainty.
• This study extends the use of cost space to feature selection in the credit scoring process.
• The proposed credit scoring framework can provide a series of Pareto-optimal credit scoring models to fit different decision-making contexts.
• The cost plot enables credit decision-makers to see the results of different credit scoring models with different risk sources and corresponding expected costs.
摘要
•An uncertainty-oriented credit scoring framework with multi-objective feature selection is developed to tackle the credit classification task under uncertainty.•This study extends the use of cost space to feature selection in the credit scoring process.•The proposed credit scoring framework can provide a series of Pareto-optimal credit scoring models to fit different decision-making contexts.•The cost plot enables credit decision-makers to see the results of different credit scoring models with different risk sources and corresponding expected costs.
论文关键词：Credit scoring,Feature selection,Multi-objective optimization,Uncertain decision making
论文评审过程：Received 18 December 2021, Revised 25 March 2022, Accepted 6 May 2022, Available online 20 May 2022, Version of Record 26 May 2022.
论文官网地址：https://doi.org/10.1016/j.elerap.2022.101155
