Wang, X;
Zhu, R;
Xue, JH;
(2026)
UC-PUAL: A universally consistent classifier of positive-unlabelled data.
Pattern Recognition
, 169
, Article 111892. 10.1016/j.patcog.2025.111892.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | UC-PUAL: A universally consistent classifier of positive-unlabelled data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.patcog.2025.111892 |
| Publisher version: | https://doi.org/10.1016/j.patcog.2025.111892 |
| Language: | English |
| Additional information: | © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210844 |
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