Effective data generation for imbalanced learning using conditional generative adversarial networks

作者：

Highlights：

• Application of conditional Generative Adversarial Networks as oversampling method.

• Generates minority class samples by recovering the training data distribution.

• Outperforms various standard oversampling algorithms.

• Performance advantage of the proposed method remains stable with higher imbalance ratios.

摘要

•Application of conditional Generative Adversarial Networks as oversampling method.•Generates minority class samples by recovering the training data distribution.•Outperforms various standard oversampling algorithms.•Performance advantage of the proposed method remains stable with higher imbalance ratios.

论文关键词：GAN,Imbalanced learning,Artificial data,Minority class

论文评审过程：Received 1 June 2017, Revised 17 August 2017, Accepted 11 September 2017, Available online 13 September 2017, Version of Record 19 September 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.030