A novel ensemble method for credit scoring: Adaption of different imbalance ratios
作者：
Highlights：
• A novel ensemble model adapting to different imbalance ratios is proposed.
• The BalanceCascade approach is extended to generate adjustable balanced sets.
• Random forest and extreme gradient boosting are used as base classifiers.
• Parameters of base classifiers are optimized with particle swarm optimization.
• The performance of the proposed model is superior to other comparative models.
摘要
•A novel ensemble model adapting to different imbalance ratios is proposed.•The BalanceCascade approach is extended to generate adjustable balanced sets.•Random forest and extreme gradient boosting are used as base classifiers.•Parameters of base classifiers are optimized with particle swarm optimization.•The performance of the proposed model is superior to other comparative models.
论文关键词：Credit scoring,Ensemble model,BalanceCascade,Imbalance ratios
论文评审过程：Received 12 October 2017, Revised 9 January 2018, Accepted 10 January 2018, Available online 11 January 2018, Version of Record 6 February 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.012
