{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:05:03Z","timestamp":1777017903169,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906165"],"award-info":[{"award-number":["61906165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s12530-023-09536-7","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T13:01:42Z","timestamp":1694005302000},"page":"1033-1042","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimizing margin distribution for online multi-label classification"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4660-2125","authenticated-orcid":false,"given":"Tingting","family":"Zhai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunyong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"9536_CR1","volume-title":"Convex Optimization","author":"SP Boyd","year":"2014","unstructured":"Boyd SP, Vandenberghe L (2014) Convex Optimization. Cambridge University Press"},{"key":"9536_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neucom.2022.04.004","volume":"492","author":"Y Chen","year":"2022","unstructured":"Chen Y, Zou C, Chen J (2022) Label-aware graph representation learning for multi-label image classification. Neurocomputing 492:50\u201361","journal-title":"Neurocomputing"},{"issue":"1","key":"9536_CR3","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TCYB.2018.2869476","volume":"50","author":"J Du","year":"2020","unstructured":"Du J, Vong C (2020) Robust online multilabel learning under dynamic changes in data distribution with labels. IEEE Trans Cybern 50(1):374\u2013385","journal-title":"IEEE Trans Cybern"},{"key":"9536_CR4","doi-asserted-by":"crossref","unstructured":"Elisseeff A, Weston J (2001) A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems (NIPS). MIT Press, pp 681\u2013687","DOI":"10.7551\/mitpress\/1120.003.0092"},{"key":"9536_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2013.07.002","volume":"203","author":"W Gao","year":"2013","unstructured":"Gao W, Zhou Z (2013) On the doubt about margin explanation of boosting. Artif Intell 203:1\u201318","journal-title":"Artif Intell"},{"issue":"3\u20134","key":"9536_CR6","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1561\/2400000013","volume":"2","author":"E Hazan","year":"2016","unstructured":"Hazan E (2016) Introduction to online convex optimization. Found Trends Optim 2(3\u20134):157\u2013325","journal-title":"Found Trends Optim"},{"key":"9536_CR7","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neucom.2021.04.112","volume":"459","author":"SCH Hoi","year":"2021","unstructured":"Hoi SCH, Sahoo D, Lu J et al (2021) Online learning: a comprehensive survey. Neurocomputing 459:249\u2013289","journal-title":"Neurocomputing"},{"key":"9536_CR8","doi-asserted-by":"crossref","unstructured":"Hsieh C, Chang K, Lin C, et\u00a0al (2008) A dual coordinate descent method for large-scale linear SVM. In: Proceedings of the 25th International Conference on Machine Learning (ICML), pp 408\u2013415","DOI":"10.1145\/1390156.1390208"},{"issue":"108","key":"9536_CR9","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.patcog.2021.108259","volume":"121","author":"J Li","year":"2022","unstructured":"Li J, Li P, Hu X et al (2022) Learning common and label-specific features for multi-label classification with correlation information. Pattern Recogn 121(108):259. https:\/\/doi.org\/10.1016\/j.patcog.2021.108259","journal-title":"Pattern Recogn"},{"issue":"3","key":"9536_CR10","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1016\/j.eswa.2014.08.036","volume":"42","author":"SM Liu","year":"2015","unstructured":"Liu SM, Chen J (2015) A multi-label classification based approach for sentiment classification. Expert Syst Appl 42(3):1083\u20131093","journal-title":"Expert Syst Appl"},{"key":"9536_CR11","first-page":"1","volume":"47","author":"J Lu","year":"2016","unstructured":"Lu J, Hoi SCH, Wang J et al (2016) Large scale online kernel learning. J Mach Learn Res 47:1\u201347","journal-title":"J Mach Learn Res"},{"issue":"107","key":"9536_CR12","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.patcog.2021.107833","volume":"113","author":"J Lv","year":"2021","unstructured":"Lv J, Wu T, Peng C et al (2021) Compact learning for multi-label classification. Pattern Recogn 113(107):833. https:\/\/doi.org\/10.1016\/j.patcog.2021.107833","journal-title":"Pattern Recogn"},{"key":"9536_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2017.01.018","volume":"121","author":"JM Moyano","year":"2017","unstructured":"Moyano JM, Galindo ELG, Ventura S (2017) MLDA: a tool for analyzing multi-label datasets. Knowl Based Syst 121:1\u20133","journal-title":"Knowl Based Syst"},{"key":"9536_CR14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.patcog.2019.06.001","volume":"95","author":"TT Nguyen","year":"2019","unstructured":"Nguyen TT, Dang MT, Luong AV et al (2019) Multi-label classification via incremental clustering on an evolving data stream. Pattern Recogn 95:96\u2013113","journal-title":"Pattern Recogn"},{"key":"9536_CR15","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.patcog.2019.01.007","volume":"90","author":"TT Nguyen","year":"2019","unstructured":"Nguyen TT, Nguyen TTT, Luong AV et al (2019) Multi-label classification via label correlation and first order feature dependance in a data stream. Pattern Recogn 90:35\u201351","journal-title":"Pattern Recogn"},{"issue":"101","key":"9536_CR16","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1016\/j.is.2021.101785","volume":"100","author":"A Omar","year":"2021","unstructured":"Omar A, Mahmoud TM, Abd-El-Hafeez T et al (2021) Multi-label arabic text classification in online social networks. Inf Syst 100(101):785. https:\/\/doi.org\/10.1016\/j.is.2021.101785","journal-title":"Inf Syst"},{"issue":"6","key":"9536_CR17","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s10994-016-5613-5","volume":"106","author":"A Osojnik","year":"2017","unstructured":"Osojnik A, Panov P, Dzeroski S (2017) Multi-label classification via multi-target regression on data streams. Mach Learn 106(6):745\u2013770","journal-title":"Mach Learn"},{"key":"9536_CR18","doi-asserted-by":"crossref","unstructured":"Sahoo D, Pham Q, Lu J, et\u00a0al (2018) Online deep learning: Learning deep neural networks on the fly. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), pp 2660\u20132666","DOI":"10.24963\/ijcai.2018\/369"},{"issue":"3","key":"9536_CR19","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s10994-019-05837-8","volume":"109","author":"Z Tan","year":"2020","unstructured":"Tan Z, Tan P, Jiang Y et al (2020) Multi-label optimal margin distribution machine. Mach Learn 109(3):623\u2013642","journal-title":"Mach Learn"},{"issue":"6","key":"9536_CR20","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1007\/s10618-021-00785-1","volume":"35","author":"KM Ting","year":"2021","unstructured":"Ting KM, Wells JR, Washio T (2021) Isolation kernel: the X factor in efficient and effective large scale online kernel learning. Data Min Knowl Disc 35(6):2282\u20132312","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"9536_CR21","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s12530-016-9162-8","volume":"8","author":"R Venkatesan","year":"2017","unstructured":"Venkatesan R, Er MJ, Dave M et al (2017) A novel online multi-label classifier for high-speed streaming data applications. Evol Syst 8(4):303\u2013315","journal-title":"Evol Syst"},{"key":"9536_CR22","first-page":"3103","volume":"13","author":"Z Wang","year":"2012","unstructured":"Wang Z, Crammer K, Vucetic S (2012) Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training. J Mach Learn Res 13:3103\u20133131","journal-title":"J Mach Learn Res"},{"issue":"107","key":"9536_CR23","first-page":"583","volume":"109","author":"R Wang","year":"2021","unstructured":"Wang R, Kwong S, Wang X et al (2021) Active k-labelsets ensemble for multi-label classification. Pattern Recogn 109(107):583","journal-title":"Pattern Recogn"},{"key":"9536_CR24","doi-asserted-by":"crossref","unstructured":"Wang Y, He D, Li F, et\u00a0al (2020) Multi-label classification with label graph superimposing. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, pp 12,265\u201312,272","DOI":"10.1609\/aaai.v34i07.6909"},{"key":"9536_CR25","doi-asserted-by":"crossref","unstructured":"Yeh C, Wu W, Ko W, et\u00a0al (2017) Learning deep latent space for multi-label classification. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2838\u20132844","DOI":"10.1609\/aaai.v31i1.10769"},{"key":"9536_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3164906","author":"T Zhai","year":"2022","unstructured":"Zhai T, Wang H (2022) Online passive-aggressive multilabel classification algorithms. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3164906","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"109","key":"9536_CR27","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.patcog.2022.109167","volume":"136","author":"T Zhai","year":"2023","unstructured":"Zhai T, Wang H, Tang H (2023) Joint optimization of scoring and thresholding models for online multi-label classification. Pattern Recogn 136(109):167. https:\/\/doi.org\/10.1016\/j.patcog.2022.109167","journal-title":"Pattern Recogn"},{"issue":"6","key":"9536_CR28","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1109\/TKDE.2019.2897662","volume":"32","author":"T Zhang","year":"2020","unstructured":"Zhang T, Zhou Z (2020) Optimal margin distribution machine. IEEE Trans Knowl Data Eng 32(6):1143\u20131156","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9536_CR29","doi-asserted-by":"crossref","unstructured":"Zhang T, Jin H (2020) Optimal margin distribution machine for multi-instance learning. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), pp 2383\u20132389","DOI":"10.24963\/ijcai.2020\/330"},{"key":"9536_CR30","doi-asserted-by":"crossref","unstructured":"Zhang T, Zhou Z (2018) Semi-supervised optimal margin distribution machines. In: Lang J (ed) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), pp 3104\u20133110","DOI":"10.24963\/ijcai.2018\/431"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-023-09536-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-023-09536-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-023-09536-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T11:22:54Z","timestamp":1717500174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-023-09536-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9536"],"URL":"https:\/\/doi.org\/10.1007\/s12530-023-09536-7","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]},"assertion":[{"value":"8 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research does not involve Human Participants and\/or Animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human participants and\/or animals"}}]}}