  When dealing with binary classification of data with only one labeled class
data scientists employ two main approaches, namely One-Class (OC)
classification and Positive Unlabeled (PU) learning. The former only learns
from labeled positive data, whereas the latter also utilizes unlabeled data to
improve the overall performance. Since PU learning utilizes more data, we might
be prone to think that when unlabeled data is available, the go-to algorithms
should always come from the PU group. However, we find that this is not always
the case if unlabeled data is unreliable, i.e. contains limited or biased
latent negative data. We perform an extensive experimental study of a wide list
of state-of-the-art OC and PU algorithms in various scenarios as far as
unlabeled data reliability is concerned. Furthermore, we propose PU
modifications of state-of-the-art OC algorithms that are robust to unreliable
unlabeled data, as well as a guideline to similarly modify other OC algorithms.
Our main practical recommendation is to use state-of-the-art PU algorithms when
unlabeled data is reliable and to use the proposed modifications of
state-of-the-art OC algorithms otherwise. Additionally, we outline procedures
to distinguish the cases of reliable and unreliable unlabeled data using
statistical tests.
