Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree
作者：
Highlights：
• The model Combines a corrected feature selection measure and XGBoost.
• Permutation importance can correct the bias of feature importance.
• The model is validated on Chinese listed companies datasets over five metrics.
• The model is proved to outperform several benchmark techniques.
• The feature importance and partial dependence plot enhance model interpretation.
摘要
•The model Combines a corrected feature selection measure and XGBoost.•Permutation importance can correct the bias of feature importance.•The model is validated on Chinese listed companies datasets over five metrics.•The model is proved to outperform several benchmark techniques.•The feature importance and partial dependence plot enhance model interpretation.
论文关键词：Financial distress prediction,Gradient boosted decision tree,Feature importance,Permutation importance,Machine learning
论文评审过程：Received 19 February 2021, Revised 1 November 2021, Accepted 5 November 2021, Available online 19 November 2021, Version of Record 23 November 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116202
