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PUAL: A classifier on trifurcate positive-unlabelled data

Wang, X., Yang, X. ORCID: 0000-0002-9299-5951, Zhu, R. ORCID: 0000-0002-9944-0369 & Xue, J-H. ORCID: 0000-0003-1174-610X (2025). PUAL: A classifier on trifurcate positive-unlabelled data. Neurocomputing, 637, article number 130080. doi: 10.1016/j.neucom.2025.130080

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.

Publication Type: Article
Additional Information: © 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Positive-unlabelled learning, Trifurcate data, Asymmetric loss
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: Bayes Business School
Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
[thumbnail of PUAL_A_Classifier_on_Trifurcated_Positive_Unlabeled_Data__Copy_.pdf] Text - Accepted Version
This document is not freely accessible until 26 March 2026 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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