[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119491-en":3,"doc-seo-119491-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119491,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",6,"Technology","Automated machine learning for positive-unlabelled learning","Positive-Unlabelled (PU) learning aims to build classifiers from data containing labelled positives and unlabelled instances whose true labels may be positive or negative. Because many PU methods exist, identifying an effective approach for a specific task is challenging. This work introduces two Auto-ML systems for PU learning: BO-Auto-PU using Bayesian optimisation and EBO-Auto-PU using a combined evolutionary and Bayesian strategy. Experiments evaluate three Auto-ML systems across 60 datasets, demonstrating statistically significant accuracy gains over baseline methods and substantial reductions in computational time compared with the prior Auto-PU system.","Kent Academic Repository  \nSaunders, Jack D. and Freitas, Alex A. (2025) Automated machine learning for positive-unlabelled learning. Applied Intelligence, 55 (12). ISSN 0924-669X.  \nDownloaded from  \n[https://kar.kent.ac.uk/110663/](https://kar.kent.ac.uk/110663/ The University of Kent's Academic Repository KAR)[ The University of Kent's Academic Repository KAR](https://kar.kent.ac.uk/110663/ The University of Kent's Academic Repository KAR)  \nThe version of record is available from  \n[https://doi.org/10.1007/s10489-025-06706-9](https://doi.org/10.1007/s10489-025-06706-9)  \nThis document version  \nPublisher pdf  \nDOI for this version  \nLicence for this version  \nCC BY (Attribution)  \nAdditional information  \nVersions of research works  \nVersions of Record  \nIf this version is the version of record, it is the same as the published version available on the publisher's web site. Cite as the published version.  \nAuthor Accepted Manuscripts  \nIf this document is identified as the Author Accepted Manuscript it is the version after peer review but before typesetting, copy editing or publisher branding. Cite as Surname, Initial. (Year) 'Title of article'. To be published in Title of Journal , Volume and issue numbers [peer-reviewed accepted version] . Available at: DOI or URL (Accessed: date) .  \nEnquiries  \nIf you have questions about this [document contact ](document contact ResearchSupport@kent.ac.uk. Please)[ResearchSupport@kent.ac.uk](document contact ResearchSupport@kent.ac.uk. Please)[. Please](document contact ResearchSupport@kent.ac.uk. Please) include the URL of the record in KAR. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.kent.ac.uk/guides/kar-the-kent-academic-repository\\#policies](https://www.kent.ac.uk/guides/kar-the-kent-academic-repository#policies)) .  \nAutomated machine learning for positive-unlabelled learning  \nJack D. Saunders1 · Alex A. Freitas1  \nAccepted: 8 June 2025 © The Author(s) 2025  \nAbstract  \nPositive-Unlabelled (PU) learning is a field of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances, which can be in reality positive or negative, but whose label is unknown. Many PU learning methods have been proposed over the last two decades, so many so that selecting an optimal method for a given PU learning task presents a challenge. Our previous work has addressed this by proposing GA-Auto-PU, the first Automated Machine Learning (Auto-ML) system for PU learning. In this work, we propose two new PU learning Auto-ML systems: BO-Auto-PU, based on a Bayesian Optimisation (BO) approach, and EBO-Auto-PU, based on a novel evolutionary/BO approach. We present an extensive evaluation of the three Auto-ML systems, comparing them to eachother and to well-established PU learning methods across 60 datasets (20 datasets, each with 3 versions) . The results of the comparison show statistically significant improvements in predictive accuracy over the baseline methods, as well as large improvements in computational time for the newly proposed Auto-PU systems over the original Auto-PU system.  \nKeywords Positive-unlabelled learning · Automated Machine Learning (Auto-ML) · Bayesian optimisation · Genetic algorithm · Classification  \n1 Introduction  \nPositive-Unlabelled (PU) learning is a growing field of machine learning that aims to learn classifiers from data consisting of a set of labelled positive instances and a set ofunlabelled instances which can be either positive or negative, but whose label is unknown [1] . This is an important field because, in real-world applications, obtaining a fully labelled dataset is often very expensive or impractical [1] .  \nTo address this issue, many methods have been proposed to learn classifiers from partially labelled data [1, 2]; including semi-supervised classification methods that learn from data with relativ","cbCail0FeDwuAKEU","https://ap.wps.com/l/cbCail0FeDwuAKEU","pdf",3129843,1,24,"English","en",105,"# Abstract\n# 1 Introduction\n## Positive-Unlabelled (PU) learning background\n## Relation to semi-supervised learning\n## Real-world applications and motivation","[{\"question\":\"What problem does positive-unlabelled (PU) learning address?\",\"answer\":\"PU learning trains classifiers using labelled positive instances and unlabelled instances, where unlabelled data may be positive or negative but its label is unknown.\"},{\"question\":\"Why is selecting a PU learning method challenging?\",\"answer\":\"Many PU learning methods have been proposed, so choosing the most suitable one for a given PU task becomes difficult.\"},{\"question\":\"What new Auto-ML systems are proposed in this work?\",\"answer\":\"The paper proposes BO-Auto-PU (Bayesian optimisation based) and EBO-Auto-PU (an evolutionary approach combined with Bayesian optimisation).\"},{\"question\":\"How were the proposed systems evaluated and what were the results?\",\"answer\":\"They were compared across 60 datasets (with multiple versions) against the original Auto-PU system and established PU methods. Results show statistically significant predictive accuracy improvements and large computational time reductions for the newly proposed systems.\"}]","Automated machine learning for positive-unlabelled learning | PDF",1785724598,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"automated-machine-learning-for-positive-unlabelled-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automated-machine-learning-for-positive-unlabelled-learning/119491/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does positive-unlabelled (PU) learning address?","Question",{"text":75,"@type":76},"PU learning trains classifiers using labelled positive instances and unlabelled instances, where unlabelled data may be positive or negative but its label is unknown.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is selecting a PU learning method challenging?",{"text":80,"@type":76},"Many PU learning methods have been proposed, so choosing the most suitable one for a given PU task becomes difficult.",{"name":82,"@type":73,"acceptedAnswer":83},"What new Auto-ML systems are proposed in this work?",{"text":84,"@type":76},"The paper proposes BO-Auto-PU (Bayesian optimisation based) and EBO-Auto-PU (an evolutionary approach combined with Bayesian optimisation).",{"name":86,"@type":73,"acceptedAnswer":87},"How were the proposed systems evaluated and what were the results?",{"text":88,"@type":76},"They were compared across 60 datasets (with multiple versions) against the original Auto-PU system and established PU methods. 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