  In real-world classification problems, the class balance in the training
dataset does not necessarily reflect that of the test dataset, which can cause
significant 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 no
labeled data is available from the test domain. In this paper, we propose to
estimate the class ratio in the test dataset by matching probability
distributions of training and test input data. We demonstrate the utility of
the proposed approach through experiments.
