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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Online learning, characterized by its feature space\u2019s adaptability over time, has emerged as a flexible learning paradigm that has attracted widespread attention. However, existing online learning methods often overlook the distributional differences between instances and the presence of label noise in streaming data, thus significantly hindering the effectiveness and robustness of these algorithms. To overcome these challenges, we propose an online confidence learning algorithm for noisy labeled features, which aims to achieve robustness against arbitrary data streams and noisy labels. It employs two new strategies: online confidence inference, which applies the principle of empirical risk minimization to identify inconsistencies in spatial distributions, and geometric structure learning, which utilizes dynamic instance confidence to compute disparities between instances and their labels. Empirical findings demonstrate that our label correction mechanism enhances classification accuracy more effectively across various types of noisy labels (i.e., symmetric, asymmetric, and flipped). Additionally, a case study on image datasets was conducted to illustrate in detail the effectiveness of our OLNLS algorithm. Code is released in\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Zhuosd\/OLNLS\">https:\/\/github.com\/Zhuosd\/OLNLS<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3734875","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T15:37:31Z","timestamp":1746718651000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Online Learning for Noisy Labeled Streams"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6864-2491","authenticated-orcid":false,"given":"Jinjie","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Cyber Security, Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5610-005X","authenticated-orcid":false,"given":"Shengda","family":"Zhuo","sequence":"additional","affiliation":[{"name":"College of Cyber Security, Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, Chicago, Illinois, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-559X","authenticated-orcid":false,"given":"Changdong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9551-022X","authenticated-orcid":false,"given":"Shuqiang","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Cyber Security, Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013232"},{"key":"e_1_3_2_3_2","first-page":"508","volume-title":"ICANN","author":"Beyazit Ege","year":"2018","unstructured":"Ege Beyazit, Matin Hosseini, Anthony Maida, and Xindong Wu. 2018. 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