Real-value negative selection over-sampling for imbalanced data set learning
作者：
Highlights：
• As an over-sampling method, RNSO does not require minority class instance available.
• The generation of artificial minority class instances only relies on majority class.
• RNSO can effectively avoid the generation of noisy instances and duplicated instances.
• RNS can solve imbalanced classification task without any modification of classifier.
• RNSO-based approach obtains better imbalanced classification results than other ones.
摘要
•As an over-sampling method, RNSO does not require minority class instance available.•The generation of artificial minority class instances only relies on majority class.•RNSO can effectively avoid the generation of noisy instances and duplicated instances.•RNS can solve imbalanced classification task without any modification of classifier.•RNSO-based approach obtains better imbalanced classification results than other ones.
论文关键词：Imbalanced data set,Over-sampling technique,Real-value negative selection,Under-sampling
论文评审过程：Received 5 June 2018, Revised 5 April 2019, Accepted 6 April 2019, Available online 6 April 2019, Version of Record 9 April 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.011
