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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Online learning, where feature spaces can change over time, offers a flexible learning paradigm that has attracted considerable attention. However, it still faces three significant challenges. First, the heterogeneity of real-world data streams with mixed feature types presents challenges for traditional parametric modeling. Second, data stream distributions can shift over time, causing an abrupt and substantial decline in model performance. Additionally, the time and cost constraints make it infeasible to label every data instance in a supervised setting. To overcome these challenges, we propose a new algorithm\n            <jats:italic toggle=\"yes\">Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features<\/jats:italic>\n            (OL-MDISF), which aims to relax restrictions on both feature types, data distribution, and supervision information. Our approach involves utilizing copula models to create a comprehensive latent space, employing an adaptive sliding window for detecting drift points to ensure model stability, and establishing label proximity information based on geometric structural relationships. To demonstrate the model\u2019s efficiency and effectiveness, we provide theoretical analysis and comprehensive experimental results.\n          <\/jats:p>","DOI":"10.1145\/3744712","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:59:16Z","timestamp":1750334356000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5610-005X","authenticated-orcid":false,"given":"Shengda","family":"Zhuo","sequence":"first","affiliation":[{"name":"College of Cyber Security, Jinan University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7788-9202","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5357-6623","authenticated-orcid":false,"given":"Yi","family":"He","sequence":"additional","affiliation":[{"name":"William &amp; Mary, Williamsburg, Virginia, USA"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/1196418"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013232"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/10654.001.0001"},{"issue":"3","key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1111\/j.1467-9868.2009.00698.x","article-title":"On-line expectation\u2013maximization algorithm for latent data models","volume":"71","author":"Capp\u00e9 Olivier","year":"2009","unstructured":"Olivier Capp\u00e9 and Eric Moulines. 2009. 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