A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems
作者：
Highlights：
• A novel local competence definition for imbalance dynamic selection classification.
• An evaluation of the classification complexity of credit scoring datasets.
• A reduction of a dynamic selection technique to a static selection approach.
• An evaluation of the prediction performance of RMkNN in comparison with regular kNN to imbalanced credit scoring datasets.
摘要
•A novel local competence definition for imbalance dynamic selection classification.•An evaluation of the classification complexity of credit scoring datasets.•A reduction of a dynamic selection technique to a static selection approach.•An evaluation of the prediction performance of RMkNN in comparison with regular kNN to imbalanced credit scoring datasets.
论文关键词：Credit scoring,Imbalanced learning,Dynamic Selection Classification
论文评审过程：Received 10 October 2019, Revised 12 February 2020, Accepted 2 March 2020, Available online 4 March 2020, Version of Record 8 April 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113351
