Invariance assumptions for class distribution estimation
Dirk Tasche
We study the problem of class distribution estimation under dataset shift. On
the training dataset, both features and class labels are observed while on the
test dataset only the features can be observed. The task then is the estimation
of the distribution of the class labels, i.e. the estimation of the class prior
probabilities, in the test dataset. Assumptions of invariance between the
training joint distribution of features and labels and the test distribution
can considerably facilitate this task. We discuss the assumptions of covariate
shift, factorizable joint shift, and sparse joint shift and their implications
for class distribution estimation.