{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:03:13Z","timestamp":1769716993992,"version":"3.49.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702.<\/jats:p>","DOI":"10.3233\/jifs-232627","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T12:13:52Z","timestamp":1690546432000},"page":"6229-6245","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing classification performance through multi-source online transfer learning algorithm with oversampling"],"prefix":"10.1177","volume":"45","author":[{"given":"Yi","family":"Liao","sequence":"first","affiliation":[{"name":"Hunan Vocational College of Commerce, Changsha, China"}]},{"given":"Kuangfeng","family":"Ning","sequence":"additional","affiliation":[{"name":"Hunan International Economics University, Changsha, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-232627_ref1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.artint.2014.06.003","article-title":"Online transfer learning","volume":"216","author":"Peilin","year":"2014","journal-title":"Artificial Intelligence"},{"issue":"3","key":"10.3233\/JIFS-232627_ref2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3309537","article-title":"Online heterogeneous transfer learning by knowledge transition","volume":"10","author":"Wu","year":"2019","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"10","key":"10.3233\/JIFS-232627_ref3","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-232627_ref4","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.knosys.2015.01.010","article-title":"Transfer learning using computational intelligence: a survey","volume":"80","author":"Jie","year":"2015","journal-title":"Knowledge-Based Systems"},{"issue":"8","key":"10.3233\/JIFS-232627_ref5","first-page":"1261","article-title":"Research progress of intention recognition for transfer learning","volume":"14","author":"Zhao","year":"2020","journal-title":"Technology"},{"issue":"11","key":"10.3233\/JIFS-232627_ref6","first-page":"1813","article-title":"Research progress of cross domain recommendation algorithms for knowledge transfer","volume":"14","author":"Ren","year":"2020","journal-title":"Journal of Frontiers of Computer Science and Technology"},{"key":"10.3233\/JIFS-232627_ref7","doi-asserted-by":"crossref","unstructured":"Dai W.Y. , Yang Q. , Xue G.R. et al., Boosting for transfer learning, Proceedings of the 24th International Conference on Machine learning, Corvallis, Jun 20\u201324, New York: ACM, 2007:193\u2013200.","DOI":"10.1145\/1273496.1273521"},{"issue":"5","key":"10.3233\/JIFS-232627_ref8","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TKDE.2013.111","article-title":"Adaptation regularization: a general framework for transfer learning","volume":"26","author":"Long","year":"2014","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-232627_ref9","doi-asserted-by":"crossref","unstructured":"Yao Y. and Doretto G. , Boosting for transfer learning with multiple sources, Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, Jun 13\u201318, Washington: IEEE Computer Society, 2010:1855\u20131862.","DOI":"10.1109\/CVPR.2010.5539857"},{"key":"10.3233\/JIFS-232627_ref10","unstructured":"Amini M.R. , Usunier N. and Goutte C. , Learning from multiple partially observed views \u2013 an application to multilingual text categorization, Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, Vancouver, Dec 7\u201310, Red Hook: Curran Associates, 2009:28\u201336."},{"key":"10.3233\/JIFS-232627_ref11","doi-asserted-by":"crossref","unstructured":"Eaton E. , Selective transfer between learning tasks using task-based boosting, Proceedings of the 25th AAAI Conference on Artificial Intelligence, Menlo Park: AAAI Press, 2011:337\u2013342.","DOI":"10.1609\/aaai.v25i1.7932"},{"issue":"1\/2","key":"10.3233\/JIFS-232627_ref12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10994-009-5148-0","article-title":"Multi-domain learning by confidence-weighted parameter combination","volume":"79","author":"Dredze","year":"2010","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-232627_ref13","doi-asserted-by":"crossref","unstructured":"Peng X.C. , Bai Q.X. , Xia X.D. et al., Moment matching for multi-source domain adaptation, Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, Seoul, Oct 27\u2013 Nov 2, Piscataway: IEEE, 2019:1406\u20131415.","DOI":"10.1109\/ICCV.2019.00149"},{"key":"10.3233\/JIFS-232627_ref14","unstructured":"Hoffman J. , Mohri M. and Zhang N.S. , Algorithms and theory for multiple-source adaptation, Proceedings of the Annual Conference on Neural Information Processing Systems, Montr\u00e9al, Dec 3\u20138, 2018:8256\u20138266."},{"issue":"7","key":"10.3233\/JIFS-232627_ref15","first-page":"3252","article-title":"Online heterogeneous transfer by hedge ensemble of offline and online decisions","volume":"29","author":"Yan","year":"2018","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"12","key":"10.3233\/JIFS-232627_ref16","first-page":"1922","article-title":"Online learning method for performance prediction of large scale services","volume":"11","author":"Sun","year":"2017","journal-title":"Journal of Frontiers of Computer Science and Technology"},{"issue":"9","key":"10.3233\/JIFS-232627_ref17","first-page":"1263","article-title":"Learning from imbalanced data","volume":"21","author":"He","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-232627_ref18","doi-asserted-by":"crossref","unstructured":"Vapnik V.N. The nature of statistical learning theory, Berlin, Heidelberg: Springer, 1995.","DOI":"10.1007\/978-1-4757-2440-0"},{"issue":"5","key":"10.3233\/JIFS-232627_ref19","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","article-title":"Twin support vector machines for pattern classification","volume":"29","author":"Khemchandani","year":"2007","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"7","key":"10.3233\/JIFS-232627_ref20","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/TKDE.2017.2685597","article-title":"Online transfer learning with multiple homogeneous or heterogeneous sources","volume":"29","author":"Wu","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-232627_ref21","doi-asserted-by":"crossref","first-page":"105149","DOI":"10.1016\/j.knosys.2019.105149","article-title":"Online transfer learning with multiple source domains for multi-class classification","volume":"190","author":"Kang","year":"2020","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"10.3233\/JIFS-232627_ref22","first-page":"248","article-title":"Multi-source online transfer lear ning for imbalanced target domain","volume":"17","author":"Zhou","year":"2022","journal-title":"CAAI Transactions on Intelligent Systems"},{"issue":"3","key":"10.3233\/JIFS-232627_ref23","doi-asserted-by":"publisher","first-page":"4103","DOI":"10.3233\/JIFS-222210","article-title":"MSIF: Multi-source information fusion based on information sets","volume":"44","author":"Yang","year":"2023","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-232627_ref24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TFUZZ.2023.3238803","article-title":"A Possibilistic Information Fusion-Based Unsupervised Feature Selection Method Using Information Quality Measures","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.3233\/JIFS-232627_ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-04428-w"},{"key":"10.3233\/JIFS-232627_ref26","first-page":"551","article-title":"Online passive aggressive algorithms","volume":"7","author":"Crammer","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/JIFS-232627_ref27","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"9","key":"10.3233\/JIFS-232627_ref28","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1109\/TNNLS.2017.2751612","article-title":"Classification of imbalanced data by oversampling in kernel space of support vector machines","volume":"29","author":"Mathew","year":"2018","journal-title":"IEEE Transactions on Neural Networks & Learning Systems"},{"key":"10.3233\/JIFS-232627_ref29","doi-asserted-by":"crossref","unstructured":"Venkateswara H. , Eusebio J. , Chakraborty S. et al., Deep hashing network for unsupervised domain adaptation, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21\u201326, Washington: IEEE Computer Society, 2017:5385\u20135394.","DOI":"10.1109\/CVPR.2017.572"},{"key":"10.3233\/JIFS-232627_ref30","doi-asserted-by":"crossref","unstructured":"Ringwald T. and Stiefelhagen R. , Adaptiope: a modern benchmark for unsupervised domain adaptation, Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, Jan 3\u20138, Piscataway: IEEE, 2021:101\u2013110.","DOI":"10.1109\/WACV48630.2021.00015"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232627","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T09:19:48Z","timestamp":1769678388000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":30,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232627","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,4]]}}}