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Computationally Efficient Class-Prior Estimation under Class Balance Change Using Energy Distance
Hideko KAWAKUBO Marthinus Christoffel DU PLESSIS Masashi SUGIYAMA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2016/01/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
class balance change, class-prior estimation, energy distance,
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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 semi-supervised 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.