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In real-world classification problems, the class balance in the trainingdataset does not necessarily reflect that of the test dataset, which can causesignificant estimation bias. If the class ratio of the test dataset is known,instance re-weighting or resampling allows systematical bias correction.However, learning the class ratio of the test dataset is challenging when nolabeled data is available from the test domain. In this paper, we propose toestimate the class ratio in the test dataset by matching probabilitydistributions of training and test input data. We demonstrate the utility ofthe proposed approach through experiments.

Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching