Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
作者：
Highlights：
• CADF deals with multiple diversity sources to balance accuracy and interpretability.
• To enhance diversity, CADF merges different correlated-adjusted decision trees.
• Results suggest CADF can compete in accuracy with much more complex ensemble models.
• Superior accuracy of CADF is tested through different measures and statistical tests.
• Oppositely to ‘black-box’ models, CADF produces logical, human understanding rules.
摘要
•CADF deals with multiple diversity sources to balance accuracy and interpretability.•To enhance diversity, CADF merges different correlated-adjusted decision trees.•Results suggest CADF can compete in accuracy with much more complex ensemble models.•Superior accuracy of CADF is tested through different measures and statistical tests.•Oppositely to ‘black-box’ models, CADF produces logical, human understanding rules.
论文关键词：Ensemble strategies,Credit scoring,Decision forests,Diversity,Gradient boosting,Random forests
论文评审过程：Available online 5 March 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.02.042
