{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:34:20Z","timestamp":1773693260015,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,11,30]]},"abstract":"<jats:p>One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.<\/jats:p>","DOI":"10.1145\/3230708","type":"journal-article","created":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T13:20:16Z","timestamp":1542115216000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8864-0314","authenticated-orcid":false,"given":"Ali","family":"Anaissi","sequence":"first","affiliation":[{"name":"The University of Sydney, Camperdown, NSW, Australia"}]},{"given":"Nguyen Lu Dang","family":"Khoa","sequence":"additional","affiliation":[{"name":"DATA61 | CSIRO, Eveleigh, NSW, Australia"}]},{"given":"Thierry","family":"Rakotoarivelo","sequence":"additional","affiliation":[{"name":"DATA61 | CSIRO, Eveleigh, NSW, Australia"}]},{"given":"Mehrisadat Makki","family":"Alamdari","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"DATA61 | CSIRO, Eveleigh, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2018,11,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/APWCCSE.2015.7476236"},{"key":"e_1_2_1_2_1","first-page":"e0157330","article-title":"Ensemble feature learning of genomic data using support vector machine","volume":"11","author":"Anaissi Ali","year":"2016","unstructured":"Ali Anaissi , Madhu Goyal , Daniel R Catchpoole , Ali Braytee , and Paul J Kennedy . 2016 . Ensemble feature learning of genomic data using support vector machine . Public Library of Science One (PLOS One) 11 , 6 (2016), e0157330 . Ali Anaissi, Madhu Goyal, Daniel R Catchpoole, Ali Braytee, and Paul J Kennedy. 2016. Ensemble feature learning of genomic data using support vector machine. Public Library of Science One (PLOS One) 11, 6 (2016), e0157330.","journal-title":"Public Library of Science One (PLOS One)"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_4"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70087-8_51"},{"key":"e_1_2_1_5_1","volume-title":"Structural Health Monitoring","author":"Balageas Daniel","unstructured":"Daniel Balageas , Claus-Peter Fritzen , and Alfredo G\u00fcemes . 2010. Structural Health Monitoring , Vol. 90 . John Wiley 8 Sons. Daniel Balageas, Claus-Peter Fritzen, and Alfredo G\u00fcemes. 2010. Structural Health Monitoring, Vol. 90. 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