Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning
作者：
Highlights：
• New method for generating artificial data using self-organizing maps.
• Provides a simple and safe way to deal with imbalanced datasets.
• Generates within-cluster and between cluster synthetic samples.
• Improves performance of classifiers and outperforms various oversampling methods.
摘要
•New method for generating artificial data using self-organizing maps.•Provides a simple and safe way to deal with imbalanced datasets.•Generates within-cluster and between cluster synthetic samples.•Improves performance of classifiers and outperforms various oversampling methods.
论文关键词：
论文评审过程：Received 1 November 2016, Revised 14 February 2017, Accepted 30 March 2017, Available online 31 March 2017, Version of Record 7 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.073
