  Positive-unlabeled (PU) learning aims to train a classifier using the data
containing only labeled-positive instances and unlabeled instances. However,
existing PU learning methods are generally hard to achieve satisfactory
performance on trifurcate data, where the positive instances distribute on both
sides of the negative instances. To address this issue, firstly we propose a PU
classifier with asymmetric loss (PUAL), by introducing a structure of
asymmetric loss on positive instances into the objective function of the global
and local learning classifier. Then we develop a kernel-based algorithm to
enable PUAL to obtain non-linear decision boundary. We show that, through
experiments on both simulated and real-world datasets, PUAL can achieve
satisfactory classification on trifurcate data.
