Computationally Efficient Class-Prior Estimation under Class Balance Change Using Energy Distance

In many real-world classification problems, the class balance often changes between training and test datasets, due to sample selection bias or the non-stationarity of the environment. Naive classifier training under such changes of class balance systematically yields a biased solution. It is known that such a systematic bias can be corrected by weighted training according to the test class balance. However, the test class balance is often unknown in practice. In this paper, we consider a semisupervised learning setup where labeled training samples and unlabeled test samples are available and propose a class balance estimator based on the energy distance. Through experiments, we demonstrate that the proposed method is computationally much more efficient than existing approaches, with comparable accuracy.