Coverage-based resampling: Building robust consolidated decision trees
作者：
Highlights：
• Coverage-based resampling determines the number of samples to be used based on dataset’s class distribution.
• Consolidated trees achieve better results for most performance measures when using higher coverage values.
• CTC ranks first against multiple genetics-based and classical algorithms for rule induction.
• CTC combined with SMOTE tops state of the art techniques designed to tackle class imbalance.
摘要
•Coverage-based resampling determines the number of samples to be used based on dataset’s class distribution.•Consolidated trees achieve better results for most performance measures when using higher coverage values.•CTC ranks first against multiple genetics-based and classical algorithms for rule induction.•CTC combined with SMOTE tops state of the art techniques designed to tackle class imbalance.
论文关键词：Comprehensibility,Consolidated decision trees,Class imbalance,Resampling,Inner ensembles
论文评审过程：Received 3 July 2014, Revised 23 December 2014, Accepted 24 December 2014, Available online 9 January 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2014.12.023
