Verifying the Assumption of Selected Completely at Random in Positive-Unlabeled Learning

The core message of this article is to propose a relatively simple and computationally fast test that can be used to determine whether the observed positive-unlabeled (PU) data meet the Selected Completely at Random (SCAR) assumption, which is a crucial step in choosing the appropriate PU learning algorithm.