OBGAN: Minority oversampling near borderline with generative adversarial networks
作者：
Highlights：
• OBGAN: A novel minority oversampling method with GAN for class imbalance problems.
• OBGAN generates data from minority region, but near the borderline.
• A novel GAN architecture with two discriminators and a loss function is proposed.
• Experimental results showed the proposed OBGAN outperformed benchmark methods.
• Hyperparameters were analyzed to be not too sensitive to achieve good performances.
摘要
•OBGAN: A novel minority oversampling method with GAN for class imbalance problems.•OBGAN generates data from minority region, but near the borderline.•A novel GAN architecture with two discriminators and a loss function is proposed.•Experimental results showed the proposed OBGAN outperformed benchmark methods.•Hyperparameters were analyzed to be not too sensitive to achieve good performances.
论文关键词：Class imbalance problem,Oversampling,Generative learning,Deep learning,Neural networks,Generative adversarial networks
论文评审过程：Received 19 March 2021, Revised 7 January 2022, Accepted 18 February 2022, Available online 26 February 2022, Version of Record 5 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116694
