Wang, Xiaoke;
Yang, Xiaochen;
Zhu, Rui;
Xue, Jing-Hao;
(2025)
PUAL: A classifier on trifurcate positive-unlabelled data.
Neurocomputing
, 637
, Article 130080. 10.1016/j.neucom.2025.130080.
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XiaokeWang-NEUCOM-2025.pdf - Accepted Version Access restricted to UCL open access staff until 27 March 2026. Download (3MB) |
Abstract
Positive-unlabelled (PU) learning aims to train a classifier using the data containing only labelled-positive instances and unlabelled 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.
Type: | Article |
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Title: | PUAL: A classifier on trifurcate positive-unlabelled data |
DOI: | 10.1016/j.neucom.2025.130080 |
Publisher version: | https://doi.org/10.1016/j.neucom.2025.130080 |
Language: | English |
Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Positive-unlabelled learningTrifurcate dataAsymmetric loss |
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/10206928 |
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