UC-PUAL: A universally consistent classifier of positive-unlabelled data
Xiaoke Wang, Rui Zhu, Jing‐Hao Xue · Pattern Recognition · 2025
Positive-unlabelled (PU) learning is a challenging task in pattern recognition, as there are only labelled-positive instances and unlabelled instances available for the training of a classifier. The task becomes even harder when the PU data show an underlying trifurcate pattern that positive instances roughly distribute on both sides of ground-truth negative instances. To address this issue, we propose a universally consistent PU classifier with asymmetric loss (UC-PUAL) on positive instances. We also propose two three-block algorithms for non-convex optimisation to enable UC-PUAL to obtain linear and kernel-induced non-linear decision boundaries, respectively. Theoretical and experimental results verify the superiority of UC-PUAL. The code for UC-PUAL is available at https://github.com/tkks22123/UC-PUAL.