A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring
作者：
Highlights：
• A novel multi-stage ensemble model is proposed for credit scoring.
• The local outlier factor approach is extended to generate outlier-adapted training sets.
• A new dimension-reduced feature transformation improves the feature interpretability.
• The stacking-based ensemble is enhanced through self-adaptive parameter optimization.
• The proposed model outperforms benchmark ensemble models.
摘要
•A novel multi-stage ensemble model is proposed for credit scoring.•The local outlier factor approach is extended to generate outlier-adapted training sets.•A new dimension-reduced feature transformation improves the feature interpretability.•The stacking-based ensemble is enhanced through self-adaptive parameter optimization.•The proposed model outperforms benchmark ensemble models.
论文关键词：Machine learning,Ensemble model,Outlier adaptation,Feature transformation,Credit scoring
论文评审过程：Received 8 December 2019, Revised 6 August 2020, Accepted 8 August 2020, Available online 11 August 2020, Version of Record 15 August 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113872
