Rethinking Class Imbalance in Machine Learning
Ou Wu
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. This study presents a new taxonomy of class imbalance in machine
learning with a broader scope. Four other types of imbalance, namely, variance,
distance, neighborhood, and quality imbalances between/among classes, which may
exist in machine learning tasks, are summarized. Two different levels of
imbalance including global and local are also presented. Theoretical analysis
is used to illustrate the significant impact of the new imbalance types on
learning fairness. Moreover, our taxonomy and theoretical conclusions are used
to analyze the shortcomings of several classical methods. As an example, we
propose a new logit perturbation-based imbalance learning loss when proportion,
variance, and distance imbalances exist simultaneously. Several classical
losses become the special case of our proposed method. Meta learning is
utilized to infer the hyper-parameters related to the three types of imbalance.
Experimental results on several benchmark corpora validate the effectiveness of
the proposed method.