  Positive-unlabeled (PU) learning deals with binary classification problems
when only positive (P) and unlabeled (U) data are available. Many recent PU
methods are based on neural networks, but little has been done to develop
boosting algorithms for PU learning, despite boosting algorithms' strong
performance on many fully supervised classification problems. In this paper, we
propose a novel boosting algorithm, AdaPU, for PU learning. Similarly to
AdaBoost, AdaPU aims to optimize an empirical exponential loss, but the loss is
based on the PU data, rather than on positive-negative (PN) data. As in
AdaBoost, we learn a weighted combination of weak classifiers by learning one
weak classifier and its weight at a time. However, AdaPU requires a very
different algorithm for learning the weak classifiers and determining their
weights. This is because AdaPU learns a weak classifier and its weight using a
weighted positive-negative (PN) dataset with some negative data weights $-$ the
dataset is derived from the original PU data, and the data weights are
determined by the current weighted classifier combination, but some data
weights are negative. Our experiments showed that AdaPU outperforms neural
networks on several benchmark PU datasets, including a large-scale challenging
cyber security dataset.
