Rethinking Class Imbalance in Machine Learning

Understand

Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance.

• Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced.
• The ignorance of the proportion imbalance will result in unfairness between/among classes and poor generalization capability.
• Previous literature has presented numerous methods for either theoretical/empirical analysis or new methods for imbalance learning.