Minority oversampling for imbalanced ordinal regression

作者：

Highlights：

• Finds a clear risk of over generalization when oversampling imbalanced ordinal regression.

• Proposes a generation direction-aware synthetic minority oversampling algorithm.

• Mechanism of protecting ordinal sample structure is established.

• Superior performance than the state of the art over various ordinal regression classifier.

摘要

•Finds a clear risk of over generalization when oversampling imbalanced ordinal regression.•Proposes a generation direction-aware synthetic minority oversampling algorithm.•Mechanism of protecting ordinal sample structure is established.•Superior performance than the state of the art over various ordinal regression classifier.

论文关键词：Imbalanced classification,Oversampling,Ordinal sample structure,Ordinal regression

论文评审过程：Received 29 August 2018, Revised 12 December 2018, Accepted 16 December 2018, Available online 21 December 2018, Version of Record 23 January 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2018.12.021