  Learning from positive and unlabeled data or PU learning is the setting where
a learner only has access to positive examples and unlabeled data. The
assumption is that the unlabeled data can contain both positive and negative
examples. This setting has attracted increasing interest within the machine
learning literature as this type of data naturally arises in applications such
as medical diagnosis and knowledge base completion. This article provides a
survey of the current state of the art in PU learning. It proposes seven key
research questions that commonly arise in this field and provides a broad
overview of how the field has tried to address them.
