Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. (arXiv:2201.13192v2 [stat.ML] UPDATED)

Positive-unlabeled (PU) learning aims at learning a binary classifier from
only positive and unlabeled training data. Recent approaches addressed this
problem via cost-sensitive learning by developing unbiased loss functions, and
their performance was later improved by iterative pseudo-labeling solutions.
However, such two-step procedures are vulnerable to incorrectly estimated
pseudo-labels, as errors are propagated in later iterations when a new model is
trained on erroneous predictions. To prevent such confirmation bias, we propose
PUUPL, a novel loss-agnostic training procedure for PU …

arxiv learning positive uncertainty