Resampling algorithms based on sample concatenation for imbalance learning

作者：

Highlights：

• Sample concatenation is introduced and analyzed for imbalance learning.

• Two resampling algorithms based on sample concatenation are proposed.

• Data complexity is used to measure the classification difficulty of imbalanced data.

• A superior performance of the proposed algorithms is verified via experiments.

摘要

•Sample concatenation is introduced and analyzed for imbalance learning.•Two resampling algorithms based on sample concatenation are proposed.•Data complexity is used to measure the classification difficulty of imbalanced data.•A superior performance of the proposed algorithms is verified via experiments.

论文关键词：Sample concatenation,Resampling,Class imbalance,Class overlap,Majority class

论文评审过程：Received 20 July 2021, Revised 4 February 2022, Accepted 12 March 2022, Available online 23 March 2022, Version of Record 2 April 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108592