A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment
作者：
Highlights：
• A novel DES model for imbalanced data set is proposed for credit scoring.
• The synthetic minority over-sampling technique is used to balance the training set.
• The meta-training process of META-DES is used to evaluate classifiers competence.
• The instances in competent region are weighted to enhance minority influence.
• The strategy of DESKNN is used to trade off classifiers’ competence and diversity.
摘要
•A novel DES model for imbalanced data set is proposed for credit scoring.•The synthetic minority over-sampling technique is used to balance the training set.•The meta-training process of META-DES is used to evaluate classifiers competence.•The instances in competent region are weighted to enhance minority influence.•The strategy of DESKNN is used to trade off classifiers’ competence and diversity.
论文关键词：Credit risk assessment,Dynamic ensemble selection (DES),Imbalanced classification,Resampling techniques
论文评审过程：Received 28 May 2020, Revised 30 July 2020, Accepted 3 September 2020, Available online 16 September 2020, Version of Record 19 September 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106462
