A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: An application in credit scoring

作者：

Highlights：

• A novel multi-stage hybrid model is proposed and applied to credit scoring.

• Multi-population niche GA (MPNGA) is proposed to improve search efficiency.

• Feature/classifier selection enables the acquisition of optimal subset.

• The stacking-based ensemble is constructed to enhance predictive effectiveness.

• The proposed model is validated on five datasets over four performance metrics.

摘要

•A novel multi-stage hybrid model is proposed and applied to credit scoring.•Multi-population niche GA (MPNGA) is proposed to improve search efficiency.•Feature/classifier selection enables the acquisition of optimal subset.•The stacking-based ensemble is constructed to enhance predictive effectiveness.•The proposed model is validated on five datasets over four performance metrics.

论文关键词：Machine learning,Multi-stage hybrid model,Feature selection,Classifier selection,Credit scoring

论文评审过程：Received 25 March 2018, Revised 12 December 2018, Accepted 13 December 2018, Available online 14 December 2018, Version of Record 20 December 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.12.020