[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125630-en":3,"doc-seo-125630-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125630,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Online automated machine learning for class imbalanced data streams","Automated machine learning (AutoML) has excelled in offline class-imbalance scenarios where data remain static, but many real applications operate on evolving data streams with skewed class distributions. Such tasks demand instant processing of streaming instances and continuous adaptation to distribution changes. Existing studies separately address class imbalance in static datasets or concept drift in streams. This work proposes UEvoAutoML and OEvoAutoML by integrating adaptive resampling into an online AutoML framework, evaluated on synthetic and real-world streams.","University of Birmingham  \nOnline automated machine learning for class imbalanced data streams  \nWang, Zhaoyang; Wang, Shuo  \nDOI:  \n10.1109/IJCNN54540.2023.10191926  \nLicense:  \nOther (please specify with Rights Statement)  \nDocument Version  \nPeer reviewed version  \nCitation for published version (Harvard):  \nWang, Z & Wang, S 2023, Online automated machine learning for class imbalanced data streams. in 2023 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE), pp. 1-8, International Joint Conference on Neural Networks, Queensland, Australia, 18/06/23 . [https://doi.org/10.1109/IJCNN54540.2023.10191926](https://doi.org/10.1109/IJCNN54540.2023.10191926)  \nLink to publication on Research at Birmingham portal  \nPublisher Rights Statement:  \n© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Aug. 2026  \nOnline automated machine learning for class imbalanced data streams  \n1st Zhaoyang Wang School of Computer Science University of Birmingham Birmingham, UK [zxw180@student.bham.ac.uk](zxw180@student.bham.ac.uk)  \n2nd Shuo Wang* School of Computer Science University of Birmingham Birmingham, UK [s.wang.2@bham.ac.uk](s.wang.2@bham.ac.uk)  \nAbstract—Automated machine learning (AutoML) has achieved great success in offline class imbalance learning where data are static. However, many real world applications data nowadays tend to evolve over time in the form of data streams and involve class imbalance distributions, e.g., intrusion detection, fault diagnosis systems, and fraud detection. These learning tasks require AutoML processing the instances instantly and adapting to the dynamic data changes. Nevertheless, existing AutoML research either only focuses on class imbalance in static data sets, or discusses data streams with concept drift. No existing work studied the joint learning challenges of class imbalance and online data stream learning in AutoML. To close the gap, this paper focuses on learning dynamic data streams with a skewed class distribution in AutoML. In this paper, we propose two new AutoML approaches, UEvoAutoML and OEvoAutoML, whi","cbCaikj52fOcb3or","https://ap.wps.com/l/cbCaikj52fOcb3or","pdf",486900,1,9,"English","en",105,"# Abstract\n# Index Terms\n# Introduction","[{\"question\":\"Why does class imbalance become more challenging in online data streams?\",\"answer\":\"In streams, data arrive continuously and may be unbounded, requiring one-by-one online learning. When class distributions are skewed, learners can develop bias and poor generalization while also needing to adapt to ongoing changes over time.\"},{\"question\":\"What gap does the paper address in AutoML research?\",\"answer\":\"Prior work largely treats class imbalance for static datasets or focuses on data streams with concept drift. The paper targets the joint learning challenges of both class imbalance and online stream learning within AutoML.\"},{\"question\":\"What are UEvoAutoML and OEvoAutoML, and how are they evaluated?\",\"answer\":\"The paper proposes two AutoML approaches, UEvoAutoML and OEvoAutoML, integrating adaptive resampling into an existing online AutoML framework. Performance is tested on synthetic imbalanced streams under stationary and non-stationary scenarios and on five real-world data streams.\"}]","Online automated machine learning for class imbalanced data streams | PDF",1785900306,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"online-automated-machine-learning-for-class-imbalanced-data-streams","",{"@graph":36,"@context":85},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/online-automated-machine-learning-for-class-imbalanced-data-streams/125630/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does class imbalance become more challenging in online data streams?","Question",{"text":75,"@type":76},"In streams, data arrive continuously and may be unbounded, requiring one-by-one online learning. When class distributions are skewed, learners can develop bias and poor generalization while also needing to adapt to ongoing changes over time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What gap does the paper address in AutoML research?",{"text":80,"@type":76},"Prior work largely treats class imbalance for static datasets or focuses on data streams with concept drift. The paper targets the joint learning challenges of both class imbalance and online stream learning within AutoML.",{"name":82,"@type":73,"acceptedAnswer":83},"What are UEvoAutoML and OEvoAutoML, and how are they evaluated?",{"text":84,"@type":76},"The paper proposes two AutoML approaches, UEvoAutoML and OEvoAutoML, integrating adaptive resampling into an existing online AutoML framework. 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