Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets
作者：
Highlights：
• A new oversampling method for imbalanced dataset classification is presented.
• It clusters the minority class and identifies borderline minority instances.
• Considering majority class during minority class clustering improves oversampling.
• Cluster size after oversampling should be dependent on its misclassification error.
• Generated synthetic instances improved subsequent classification.
摘要
•A new oversampling method for imbalanced dataset classification is presented.•It clusters the minority class and identifies borderline minority instances.•Considering majority class during minority class clustering improves oversampling.•Cluster size after oversampling should be dependent on its misclassification error.•Generated synthetic instances improved subsequent classification.
论文关键词：Imbalanced dataset,Classification,Clustering,Oversampling
论文评审过程：Received 22 June 2015, Revised 23 October 2015, Accepted 25 October 2015, Available online 6 November 2015, Version of Record 21 November 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.10.031
