G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE

作者：

Highlights：

• Improved oversampling algorithm (G-SOMO) to address the imbalance-learning problem.

• It uses a Self-Organizing Map to identify optimal areas to generate artificial data.

• The Geometric-SMOTE algorithm is used to generate the artificial instances.

• G-SOMO is tested in 69 datasets and consistently outperforms the benchmarks.

摘要

•Improved oversampling algorithm (G-SOMO) to address the imbalance-learning problem.•It uses a Self-Organizing Map to identify optimal areas to generate artificial data.•The Geometric-SMOTE algorithm is used to generate the artificial instances.•G-SOMO is tested in 69 datasets and consistently outperforms the benchmarks.

论文关键词：Machine learning,Classification,Imbalanced learning,Oversampling,G-SMOTE,SOM

论文评审过程：Received 12 January 2020, Revised 23 March 2021, Accepted 16 May 2021, Available online 6 June 2021, Version of Record 11 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115230