An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling

作者：

Highlights：

• A novel data partition hybrid sampling is proposed.

• A new over-sampling is proposed to strengthen the recognition of the minority class.

• A model dynamic selection strategy is presented.

摘要

•A novel data partition hybrid sampling is proposed.•A new over-sampling is proposed to strengthen the recognition of the minority class.•A model dynamic selection strategy is presented.

论文关键词：Imbalanced classification,Ensemble learning,Data partition hybrid sampling,Model dynamic selection

论文评审过程：Received 23 December 2019, Revised 11 June 2020, Accepted 12 June 2020, Available online 25 June 2020, Version of Record 6 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113660