Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
作者：
Highlights：
• A thorough analysis of oversampling for handling multi-class imbalanced datasets.
• Proposition to detect underlying structures and example types in considered classes.
• Smart oversampling based on extracted knowledge about imbalance distribution types.
• In-depth insight into the importance of selecting proper examples for oversampling.
• Guidelines that allow to design efficient classifiers for multi-class imbalanced data.
摘要
Highlights•A thorough analysis of oversampling for handling multi-class imbalanced datasets.•Proposition to detect underlying structures and example types in considered classes.•Smart oversampling based on extracted knowledge about imbalance distribution types.•In-depth insight into the importance of selecting proper examples for oversampling.•Guidelines that allow to design efficient classifiers for multi-class imbalanced data.
论文关键词：Machine learning,Imbalanced classification,Multi-class imbalance,Oversampling,Minority class types
论文评审过程：Received 29 July 2015, Revised 5 March 2016, Accepted 8 March 2016, Available online 16 March 2016, Version of Record 6 May 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.03.012
