Awesome Imbalanced Learning

Class-imbalance (also known as the long-tail problem) is the fact that the classes are not represented equally in a classification problem, which is quite common in practice. For instance, fraud detection, prediction of rare adverse drug reactions and prediction gene families. Failure to account for the class imbalance often causes inaccurate and decreased predictive performance of many classification algorithms. Imbalanced learning aims to tackle the class imbalance problem to learn an unbiased model from imbalanced data.

Table of Contents

• Awesome Imbalanced Learning

• Table of Contents

• Libraries

• Python

• R

• Java

• Scalar

• Julia

• Papers

• Surveys

• Deep Learning

• Data resampling

• Cost-sensitive Learning

• Ensemble Learning

• Anomaly Detection

• Others

• Imbalanced Datasets

• Other Resources

github地址：https://github.com/ZhiningLiu1998/awesome-imbalanced-learning