A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring

作者：

Highlights：

• A new hybrid ensemble model is proposed for credit scoring.

• The outlier adaptability is enhanced by the voting-based outlier detection method.

• The under-sampling method is extended to handle data imbalance.

• The stacking-based ensemble enhances the predictive power of the proposed model.

• The proposed model outperforms the benchmark ensemble models.

摘要

•A new hybrid ensemble model is proposed for credit scoring.•The outlier adaptability is enhanced by the voting-based outlier detection method.•The under-sampling method is extended to handle data imbalance.•The stacking-based ensemble enhances the predictive power of the proposed model.•The proposed model outperforms the benchmark ensemble models.

论文关键词：Machine learning,Ensemble modeling,Outlier detection,Balanced sampling,Credit scoring

论文评审过程：Received 10 August 2020, Revised 2 December 2020, Accepted 13 February 2021, Available online 20 February 2021, Version of Record 4 March 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114744