Advancing Ensemble Learning Performance through data transformation and classifiers fusion in granular computing context
作者：
Highlights：
• We proposed a new ensemble learning framework in the setting of granular computing.
• We conducted data transformation by information granulation for in-depth training.
• We developed multi-level fusion strategies for in-depth classifiers fusion.
• The proposed framework outperforms popular ensemble learning methods.
• The proposed framework outperforms popular standard learning methods.
摘要
•We proposed a new ensemble learning framework in the setting of granular computing.•We conducted data transformation by information granulation for in-depth training.•We developed multi-level fusion strategies for in-depth classifiers fusion.•The proposed framework outperforms popular ensemble learning methods.•The proposed framework outperforms popular standard learning methods.
论文关键词：Machine learning,Ensemble learning,Classification,Bagging,Boosting,Random forests
论文评审过程：Received 21 August 2018, Revised 30 March 2019, Accepted 19 April 2019, Available online 20 April 2019, Version of Record 24 April 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.051
