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PUAL: A classifier on trifurcate positive-unlabelled data

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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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
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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