{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:24:04Z","timestamp":1773660244232,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"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":["62566001"],"award-info":[{"award-number":["62566001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004772","name":"Natural Science Foundation of Ningxia Province","doi-asserted-by":"publisher","award":["2025AAC030054"],"award-info":[{"award-number":["2025AAC030054"]}],"id":[{"id":"10.13039\/501100004772","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10618-026-01197-9","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:41:08Z","timestamp":1773657668000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-class imbalanced data stream classification algorithm based on sample weighting and adaptive oversampling"],"prefix":"10.1007","volume":"40","author":[{"given":"Meng","family":"Han","sequence":"first","affiliation":[]},{"given":"Shineng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shurong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhenlong","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Wenyan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"issue":"7","key":"1197_CR1","doi-asserted-by":"publisher","first-page":"4165","DOI":"10.1007\/s10994-023-06353-6","volume":"113","author":"G Aguiar","year":"2024","unstructured":"Aguiar G, Krawczyk B, Cano A (2024) A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. Mach Learn 113(7):4165\u20134243","journal-title":"Mach Learn"},{"key":"1197_CR2","doi-asserted-by":"crossref","unstructured":"Bernardo A, Gomes HM, Montiel J, et al (2020) C-smote: Continuous synthetic minority oversampling for evolving data streams[C]\/\/2020 IEEE International Conference on Big Data (Big Data). IEEE, 483\u2013492.","DOI":"10.1109\/BigData50022.2020.9377768"},{"key":"1197_CR3","doi-asserted-by":"crossref","unstructured":"Bernardo A, Della Valle E, Bifet A (2020) Incremental rebalancing learning on evolving data streams[C]\/\/2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 844\u2013850.","DOI":"10.1109\/ICDMW51313.2020.00121"},{"key":"1197_CR4","doi-asserted-by":"crossref","unstructured":"Bifet A, Gavalda R (2007) Learning from time-changing data with adaptive windowing[C]\/\/Proceedings of the 2007 SIAM international conference on data mining. Soc Industr Appl Math, 443\u2013448.","DOI":"10.1137\/1.9781611972771.42"},{"key":"1197_CR5","unstructured":"Bifet A, Holmes G, Pfahringer B, et al (2010) Moa: Massive online analysis, a framework for stream classification and clustering[C]\/\/Proceedings of the first workshop on applications of pattern analysis. PMLR, 44\u201350."},{"issue":"1","key":"1197_CR6","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10994-019-05840-z","volume":"109","author":"A Cano","year":"2020","unstructured":"Cano A, Krawczyk B (2020) Kappa updated ensemble for drifting data stream mining. Mach Learn 109(1):175\u2013218","journal-title":"Mach Learn"},{"issue":"7","key":"1197_CR7","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1007\/s10994-022-06168-x","volume":"111","author":"A Cano","year":"2022","unstructured":"Cano A, Krawczyk B (2022) ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Mach Learn 111(7):2561\u20132599","journal-title":"Mach Learn"},{"key":"1197_CR8","doi-asserted-by":"publisher","first-page":"118590","DOI":"10.1016\/j.eswa.2022.118590","volume":"212","author":"F Ceschin","year":"2023","unstructured":"Ceschin F, Botacin M, Gomes HM et al (2023) Fast & furious: on the modelling of malware detection as an evolving data stream. Expert Syst Appl 212:118590","journal-title":"Expert Syst Appl"},{"issue":"22","key":"1197_CR9","doi-asserted-by":"publisher","first-page":"11430","DOI":"10.1007\/s10489-024-05754-x","volume":"54","author":"W Chen","year":"2024","unstructured":"Chen W, Guo WJ, Mao WJ (2024) An adaptive over-sampling method for imbalanced data based on simultaneous clustering and filtering noisy. Appl Intell 54(22):11430\u201311449","journal-title":"Appl Intell"},{"key":"1197_CR10","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng H, Runger G, Tuv E et al (2013) A time series forest for classification and feature extraction. Inf Sci 239:142\u2013153","journal-title":"Inf Sci"},{"key":"1197_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy D, Atiya AF (2019) A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci 505:32\u201364","journal-title":"Inf Sci"},{"key":"1197_CR12","doi-asserted-by":"crossref","unstructured":"Ferreira LEB, Gomes HM, Bifet A, et al (2019) Adaptive random forests with resampling for imbalanced data streams [C]\/\/Proc of 2019 International Joint Conference on Neural Networks. Budapest: IEEE, 1\u20136.","DOI":"10.1109\/IJCNN.2019.8852027"},{"issue":"3","key":"1197_CR13","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s10994-012-5320-9","volume":"90","author":"J Gama","year":"2013","unstructured":"Gama J, Sebastiao R, Rodrigues PP (2013) On evaluating stream learning algorithms. Mach Learn 90(3):317\u2013346","journal-title":"Mach Learn"},{"key":"1197_CR14","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","volume":"106","author":"HM Gomes","year":"2017","unstructured":"Gomes HM, Bifet A, Read J et al (2017a) Adaptive random forests for evolving data stream classification [J]. Mach Learn 106:1469\u20131495","journal-title":"Mach Learn"},{"issue":"2","key":"1197_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3054925","volume":"50","author":"HM Gomes","year":"2017","unstructured":"Gomes HM, Barddal JP, Enembreck F et al (2017b) A survey on ensemble learning for data stream classification[J]. ACM Computing Surveys (CSUR) 50(2):1\u201336","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"7","key":"1197_CR16","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1007\/s10115-021-01579-z","volume":"63","author":"HM Gomes","year":"2021","unstructured":"Gomes HM, Read J, Bifet A et al (2021) Learning from evolving data streams through ensembles of random patches. Knowl Inf Syst 63(7):1597\u20131625","journal-title":"Knowl Inf Syst"},{"issue":"11","key":"1197_CR17","doi-asserted-by":"publisher","first-page":"6845","DOI":"10.1007\/s10115-024-02184-6","volume":"66","author":"M Han","year":"2024","unstructured":"Han M, Li CP, Meng FX et al (2024) An online ensemble classification algorithm for multi-class imbalanced data stream. Knowl Inf Syst 66(11):6845\u20136880","journal-title":"Knowl Inf Syst"},{"key":"1197_CR18","doi-asserted-by":"publisher","first-page":"101614","DOI":"10.1016\/j.jocs.2022.101614","volume":"61","author":"C Ireneusz","year":"2022","unstructured":"Ireneusz C (2022) Weighted ensemble with one-class classification and over-sampling and instance selection (WECOI): an approach for learning from imbalanced data streams. J Comput Sci 61:101614","journal-title":"J Comput Sci"},{"issue":"1","key":"1197_CR19","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1109\/TNNLS.2022.3183120","volume":"35","author":"BT Jiao","year":"2022","unstructured":"Jiao BT, Guo YN, Gong DW et al (2022) Dynamic ensemble selection for imbalanced data streams with concept drift. IEEE Trans Neural Netw Learn Syst 35(1):1278\u20131291","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1197_CR20","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk B, Minku LL, Gama J et al (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37:132\u2013156","journal-title":"Inf Fusion"},{"key":"1197_CR21","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.ins.2020.08.051","volume":"547","author":"DC Liang","year":"2021","unstructured":"Liang DC, Yi BC (2021) Two-stage three-way enhanced technique for ensemble learning in inclusive policy text classification. Inf Sci 547:271\u2013288","journal-title":"Inf Sci"},{"key":"1197_CR22","doi-asserted-by":"publisher","first-page":"106778","DOI":"10.1016\/j.knosys.2021.106778","volume":"215","author":"WK Liu","year":"2021","unstructured":"Liu WK, Zhang H, Ding ZY et al (2021) A comprehensive active learning method for multiclass imbalanced data streams with concept drift[J]. Knowl-Based Syst 215:106778","journal-title":"Knowl-Based Syst"},{"key":"1197_CR23","doi-asserted-by":"publisher","first-page":"105607","DOI":"10.1016\/j.engappai.2022.105607","volume":"117","author":"WK Liu","year":"2023","unstructured":"Liu WK, Zhu C, Ding ZY et al (2023) Multiclass imbalanced and concept drift network traffic classification framework based on online active learning[J]. Eng Appl Artif Intell 117:105607","journal-title":"Eng Appl Artif Intell"},{"key":"1197_CR24","doi-asserted-by":"crossref","unstructured":"Loezer L, Enembreck F, Barddal JP, et al (2020) Cost-sensitive learning for imbalanced data streams[C]\/\/Proceedings of the 35th annual ACM symposium on applied computing. 498\u2013504.","DOI":"10.1145\/3341105.3373949"},{"issue":"10","key":"1197_CR25","doi-asserted-by":"publisher","first-page":"4445","DOI":"10.1109\/TNNLS.2020.3017863","volume":"32","author":"K Malialis","year":"2020","unstructured":"Malialis K, Panayiotou CG, Polycarpou MM (2020) Online learning with adaptive rebalancing in nonstationary environments. IEEE Trans Neural Netw Learn Syst 32(10):4445\u20134459","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1197_CR26","doi-asserted-by":"publisher","first-page":"121159","DOI":"10.1016\/j.eswa.2023.121159","volume":"234","author":"J Men","year":"2023","unstructured":"Men J, Zhao C (2023) An adaptive imbalance modified online broad learning system-based fault diagnosis for imbalanced chemical process data stream. Expert Syst Appl 234:121159","journal-title":"Expert Syst Appl"},{"key":"1197_CR27","doi-asserted-by":"publisher","first-page":"112830","DOI":"10.1016\/j.knosys.2024.112830","volume":"309","author":"AM Paim","year":"2025","unstructured":"Paim AM, Enembreck F (2025) Adaptive random tree ensemble for evolving data stream classification. Knowl-Based Syst 309:112830","journal-title":"Knowl-Based Syst"},{"key":"1197_CR28","doi-asserted-by":"publisher","first-page":"111500","DOI":"10.1016\/j.knosys.2024.111500","volume":"288","author":"M Pepsi","year":"2024","unstructured":"Pepsi M, Kumar N (2024) Hybrid firefly optimised ensemble classification for drifting data streams with imbalance. Knowl-Based Syst 288:111500","journal-title":"Knowl-Based Syst"},{"issue":"21","key":"1197_CR29","doi-asserted-by":"publisher","first-page":"24908","DOI":"10.1007\/s10489-023-04886-w","volume":"53","author":"F Sadeghi","year":"2023","unstructured":"Sadeghi F, Viktor HL, Vafaie P (2023) DynaQ: online learning from imbalanced multi-class streams through dynamic sampling. Appl Intell 53(21):24908\u201324930","journal-title":"Appl Intell"},{"key":"1197_CR30","doi-asserted-by":"publisher","first-page":"110795","DOI":"10.1016\/j.knosys.2023.110795","volume":"277","author":"XM Tao","year":"2023","unstructured":"Tao XM, Guo XY, Zheng YJ et al (2023) Self-adaptive oversampling method based on the complexity of minority data in imbalanced datasets classification. Knowl-Based Syst 277:110795","journal-title":"Knowl-Based Syst"},{"key":"1197_CR31","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.future.2021.10.033","volume":"129","author":"W Ullah","year":"2022","unstructured":"Ullah W, Ullah A, Hussain T et al (2022) Artificial intelligence of things-assisted two-stream neural network for anomaly detection in surveillance big video data. Future Gener Comput Syst 129:286\u2013297","journal-title":"Future Gener Comput Syst"},{"key":"1197_CR32","doi-asserted-by":"crossref","unstructured":"Vafaie P, Viktor H, Michalowski W (2020) Multi-class imbalanced semi-supervised learning from streams through online ensembles[C]\/\/Proc of International Conference on Data Mining Workshops, Sorrento, Nov 17\u201320, 2020. Piscataway: IEEE, 867\u2013874.","DOI":"10.1109\/ICDMW51313.2020.00124"},{"issue":"10","key":"1197_CR33","doi-asserted-by":"publisher","first-page":"4802","DOI":"10.1109\/TNNLS.2017.2771290","volume":"29","author":"S Wang","year":"2018","unstructured":"Wang S, Minku LL, Yao X (2018) A systematic study of online class imbalance learning with concept drift. IEEE Trans Neural Netw Learn Syst 29(10):4802\u20134821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1197_CR34","unstructured":"Wang S, Minku LL, Yao X (2016) Dealing with multiple classes in online class imbalance learning [C]\/\/Proc of 2016 International Joint Conference on Artificial Intelligence. New Work: Morgan Kaufmann, 2118\u20132124."},{"key":"1197_CR35","doi-asserted-by":"crossref","unstructured":"Woolson RF (2007) Wilcoxon signed\u2010rank test. Wiley Encyclopedia of Clinical Trials, 1\u20133.","DOI":"10.1002\/9780471462422.eoct979"},{"key":"1197_CR36","unstructured":"Zhang Y, Du H L, Ke G, et al (2022) Dynamic weighted selective ensemble learning algorithm for imbalanced data streams[J]. The Journal of Supercomputing, 1\u201326."}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-026-01197-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-026-01197-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-026-01197-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:41:14Z","timestamp":1773657674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-026-01197-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,16]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["1197"],"URL":"https:\/\/doi.org\/10.1007\/s10618-026-01197-9","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,16]]},"assertion":[{"value":"27 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}],"article-number":"29"}}