SelectNet: Learning to Sample from the Wild for Imbalanced Data Training

no code implementations • 23 May 2019 • Yunru Liu, Tingran Gao, Haizhao Yang

Supervised learning from training data with imbalanced class sizes, a commonly encountered scenario in real applications such as anomaly/fraud detection, has long been considered a significant challenge in machine learning.

Fraud Detection