Classifying imbalanced data sets using similarity based hierarchical decomposition
作者：
Highlights：
• A novel method for imbalanced dataset classification.
• A new hierarchical classifier which does not use a fixed feature/class hierarchy.
• Uses clustering and outlier detection to construct the hierarchy.
• Shows that different feature spaces can be used to build a hierarchy.
• Successful when the class imbalanced ratio is low, classes are highly overlapping.
摘要
•A novel method for imbalanced dataset classification.•A new hierarchical classifier which does not use a fixed feature/class hierarchy.•Uses clustering and outlier detection to construct the hierarchy.•Shows that different feature spaces can be used to build a hierarchy.•Successful when the class imbalanced ratio is low, classes are highly overlapping.
论文关键词：Class imbalance problem,Hierarchical decomposition,Clustering,Outlier detection,Minority–majority classes
论文评审过程：Received 8 January 2014, Revised 17 October 2014, Accepted 27 October 2014, Available online 26 November 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.10.032
