Random Balance ensembles for multiclass imbalance learning
作者：
Highlights：
• Random Balance ensembles are extended from binary to multiple classes.
• In the first approach the proportions of the multiple are assigned randomly.
• The second approach is based on binarization techniques (one-vs-one or one-vs-all).
• Random Balance ensembles are also combined with Bagging and Boosting.
• The experiments over 52 data sets show the viability of both approaches.
摘要
•Random Balance ensembles are extended from binary to multiple classes.•In the first approach the proportions of the multiple are assigned randomly.•The second approach is based on binarization techniques (one-vs-one or one-vs-all).•Random Balance ensembles are also combined with Bagging and Boosting.•The experiments over 52 data sets show the viability of both approaches.
论文关键词：Classifier ensembles,Imbalanced data,Multiclass classification
论文评审过程：Received 26 January 2019, Revised 23 December 2019, Accepted 24 December 2019, Available online 27 December 2019, Version of Record 7 March 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.105434
