Imbalance learning using heterogeneous ensembles

作者：

Highlights：

• Using multiple balancing schemes and classification methods in imbalance learning is addressed.

• Significantly better scores are achieved by using multiple classification methods.

• Notable improvements are not observed when multiple balancing schemes are considered.

• Bagging-based ensembles provided better performance scores than simple and boosting-based ensembles.

摘要

•Using multiple balancing schemes and classification methods in imbalance learning is addressed.•Significantly better scores are achieved by using multiple classification methods.•Notable improvements are not observed when multiple balancing schemes are considered.•Bagging-based ensembles provided better performance scores than simple and boosting-based ensembles.

论文关键词：Imbalance learning,Classifier ensembles,Bagging,Boosting,Heterogeneous ensembles,Multiple balancing methods

论文评审过程：Received 5 August 2018, Revised 4 October 2019, Accepted 4 October 2019, Available online 5 October 2019, Version of Record 9 October 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113005