Wang, Xiaoke;
(2024)
Research on Positive-Unlabeled Learning.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Positive-unlabeled (PU) learning handles classification tasks on the data containing only labeled-positive instances and unlabeled instances. PU learning has been applied in many fields of observational studies. Support vector machine (SVM)-based PU learning is one of the main branches of PU learning and offers a range of advantages, e.g., the efficiency of training and the generalisation ability. Moreover, the SVM-based PU classifiers are able to generate non-linear decision boundary by employing kernel trick to capture complex relationships among features and have been shown to achieve robust performance. This study focuses on SVM-based PU classifiers and contains three contributions. Firstly we proposed global and local PU classifier with asymmetric loss (GLPUAL) with kernel trick applied for satisfactory classification on trifurcated PU datasets, where the positive set is constituted by two subsets distributing on both sides of the negative set. Secondly, to address the unsatisfactory interpretability and performance of GLPUAL on the PU datasets containing irrelevant features, we introduced L1-norm regularisation to the objective function of GLPUAL to construct a sparse classifier to remove irrelevant features. The proposed classifier is termed elastic GLPUAL (E-GLPUAL). Then a kernel-free technique was introduced to E-GLPUAL to generate non-linear decision boundary. The proposed classifier is termed elastic kernel-free GLPUAL (EKF-GLPUAL). Thirdly, we proposed class-prior-based GLPUAL (CPB-GLPUAL) by introducing a technique of unbiased PU learning to GLPUAL for better performance when the class prior is known. Besides, we explored the conditions for CPB-GLPUAL to exhibit universal consistency between the 0-1 classification risk of CPB-GLPUAL and the Bayes risk.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Research on Positive-Unlabeled Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/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/10192656 |
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