  Positive-Unlabeled (PU) learning is an analog to supervised binary
classification for the case when only the positive sample is clean, while the
negative sample is contaminated with latent instances of positive class and
hence can be considered as an unlabeled mixture. The objectives are to classify
the unlabeled sample and train an unbiased PN classifier, which generally
requires to identify the mixing proportions of positives and negatives first.
Recently, unbiased risk estimation framework has achieved state-of-the-art
performance in PU learning. This approach, however, exhibits two major
bottlenecks. First, the mixing proportions are assumed to be identified, i.e.
known in the domain or estimated with additional methods. Second, the approach
relies on the classifier being a neural network. In this paper, we propose
DEDPUL, a method that solves PU Learning without the aforementioned issues. The
mechanism behind DEDPUL is to apply a computationally cheap post-processing
procedure to the predictions of any classifier trained to distinguish positive
and unlabeled data. Instead of assuming the proportions to be identified,
DEDPUL estimates them alongside with classifying unlabeled sample. Experiments
show that DEDPUL outperforms the current state-of-the-art in both proportion
estimation and PU Classification.
