Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets

作者：

Highlights：

• A new method for oversampling very scarce training sets.

• Based on Generative Adversarial Networks and Markov Random Field models.

• Much better performance than SMOTE on simulated and real data experiment.

摘要

•A new method for oversampling very scarce training sets.•Based on Generative Adversarial Networks and Markov Random Field models.•Much better performance than SMOTE on simulated and real data experiment.

论文关键词：Classifier training,Oversampling,Generative adversarial networks,Markov random fields

论文评审过程：Received 7 October 2019, Revised 13 July 2020, Accepted 30 July 2020, Available online 3 August 2020, Version of Record 10 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113819