{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T13:11:32Z","timestamp":1785503492408,"version":"3.56.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s13042-026-03141-1","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:15:39Z","timestamp":1780467339000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting concept drift in imbalanced data streams with limited labels"],"prefix":"10.1007","volume":"17","author":[{"given":"Yanhong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suge","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"issue":"10","key":"3141_CR1","doi-asserted-by":"publisher","first-page":"9523","DOI":"10.1016\/j.jksuci.2021.11.006","volume":"34","author":"S Agrahari","year":"2022","unstructured":"Agrahari S, Singh AK (2022) Concept drift detection in data stream mining: a literature review. J King Saud Univ-Comput Inform Sci 34(10):9523\u20139540","journal-title":"J King Saud Univ-Comput Inform Sci"},{"issue":"8","key":"3141_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3587098","volume":"17","author":"B Halstead","year":"2023","unstructured":"Halstead B, Koh YS, Riddle P, Pechenizkiy M, Bifet A (2023) Combining diverse meta-features to accurately identify recurring concept drift in data streams. ACM Trans Knowl Discov Data 17(8):1\u201336","journal-title":"ACM Trans Knowl Discov Data"},{"key":"3141_CR3","doi-asserted-by":"publisher","first-page":"108632","DOI":"10.1016\/j.knosys.2022.108632","volume":"245","author":"F Bayram","year":"2022","unstructured":"Bayram F, Ahmed BS, Kassler A (2022) From concept drift to model degradation: an overview on performance-aware drift detectors. Knowl-Based Syst 245:108632","journal-title":"Knowl-Based Syst"},{"key":"3141_CR4","doi-asserted-by":"publisher","first-page":"104121","DOI":"10.1016\/j.cose.2024.104121","volume":"148","author":"S Cai","year":"2025","unstructured":"Cai S, Tang H, Chen J, Hu Y, Guo W (2025) Cdda-md: an efficient malicious traffic detection method based on concept drift detection and adaptation technique. Comput Security 148:104121","journal-title":"Comput Security"},{"key":"3141_CR5","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1016\/j.ins.2022.12.085","volume":"625","author":"RA Coelho","year":"2023","unstructured":"Coelho RA, Torres LCB, Castro CL (2023) Concept drift detection with quadtree-based spatial mapping of streaming data. Inf Sci 625:578\u2013592","journal-title":"Inf Sci"},{"key":"3141_CR6","doi-asserted-by":"publisher","first-page":"109113","DOI":"10.1016\/j.patcog.2022.109113","volume":"134","author":"H Yu","year":"2023","unstructured":"Yu H, Liu W, Lu J, Wen Y, Luo X, Zhang G (2023) Detecting group concept drift from multiple data streams. Pattern Recogn 134:109113","journal-title":"Pattern Recogn"},{"key":"3141_CR7","doi-asserted-by":"publisher","first-page":"118934","DOI":"10.1016\/j.eswa.2022.118934","volume":"213","author":"AL Su\u00e1rez-Cetrulo","year":"2023","unstructured":"Su\u00e1rez-Cetrulo AL, Quintana D, Cervantes A (2023) A survey on machine learning for recurring concept drifting data streams. Expert Syst Appl 213:118934","journal-title":"Expert Syst Appl"},{"key":"3141_CR8","doi-asserted-by":"publisher","first-page":"119946","DOI":"10.1016\/j.eswa.2023.119946","volume":"224","author":"M Karimian","year":"2023","unstructured":"Karimian M, Beigy H (2023) Concept drift handling: a domain adaptation perspective. Expert Syst Appl 224:119946","journal-title":"Expert Syst Appl"},{"key":"3141_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2021.11.023","volume":"585","author":"H Guo","year":"2022","unstructured":"Guo H, Li H, Ren Q, Wang W (2022) Concept drift type identification based on multi-sliding windows. Inf Sci 585:1\u201323","journal-title":"Inf Sci"},{"issue":"2","key":"3141_CR10","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1002\/widm.1327","volume":"10","author":"H Hu","year":"2020","unstructured":"Hu H, Kantardzic M, Sethi TS (2020) No free lunch theorem for concept drift detection in streaming data classification: a review. Wiley Interdis Revi Data Mining Knowledge Discov 10(2):1327","journal-title":"Wiley Interdis Revi Data Mining Knowledge Discov"},{"key":"3141_CR11","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 90:317\u2013346","journal-title":"Mach Learn"},{"issue":"12","key":"3141_CR12","first-page":"2346","volume":"31","author":"J Lu","year":"2019","unstructured":"Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G (2019) Learning under concept drift: a review. IEEE Trans Knowl Data Eng 31(12):2346\u20132363","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"3141_CR13","doi-asserted-by":"publisher","first-page":"4351","DOI":"10.1007\/s10994-022-06188-7","volume":"112","author":"V Cerqueira","year":"2023","unstructured":"Cerqueira V, Gomes HM, Bifet A, Torgo L (2023) Studd: a student-teacher method for unsupervised concept drift detection. Mach Learn 112(11):4351\u20134378","journal-title":"Mach Learn"},{"key":"3141_CR14","doi-asserted-by":"crossref","unstructured":"Pham T, Kottke D, Krempl G, Sick B (2022) Stream-based active learning for sliding windows under the influence of verification latency. Machine Learning, 1\u201326","DOI":"10.1007\/s10994-021-06099-z"},{"key":"3141_CR15","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ins.2016.03.034","volume":"355","author":"E Lughofer","year":"2016","unstructured":"Lughofer E, Weigl E, Heidl W, Eitzinger C, Radauer T (2016) Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances. Inf Sci 355:127\u2013151","journal-title":"Inf Sci"},{"key":"3141_CR16","doi-asserted-by":"crossref","unstructured":"Wang S, Minku LL, Ghezzi D, Caltabiano D, Tino P, Yao X (2013) Concept drift detection for online class imbalance learning. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1\u201310 . IEEE","DOI":"10.1109\/IJCNN.2013.6706768"},{"issue":"10","key":"3141_CR17","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 Learning Syst 29(10):4802\u20134821","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"key":"3141_CR18","doi-asserted-by":"publisher","first-page":"102325","DOI":"10.1016\/j.adhoc.2020.102325","volume":"111","author":"Y Yuan","year":"2021","unstructured":"Yuan Y, Wang Z, Wang W (2021) Unsupervised concept drift detection based on multi-scale slide windows. Ad Hoc Netw 111:102325","journal-title":"Ad Hoc Netw"},{"issue":"6","key":"3141_CR19","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1002\/widm.1381","volume":"10","author":"RN Gemaque","year":"2020","unstructured":"Gemaque RN, Costa AFJ, Giusti R, Dos Santos EM (2020) An overview of unsupervised drift detection methods. Wiley Interdiscip Rev Data Mining Knowledge Disc 10(6):1381","journal-title":"Wiley Interdiscip Rev Data Mining Knowledge Disc"},{"key":"3141_CR20","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.procs.2015.07.284","volume":"53","author":"TS Sethi","year":"2015","unstructured":"Sethi TS, Kantardzic M (2015) Don\u2019t pay for validation: detecting drifts from unlabeled data using margin density. Procedia Comput Sci 53:103\u2013112","journal-title":"Procedia Comput Sci"},{"key":"3141_CR21","doi-asserted-by":"crossref","unstructured":"Dos\u00a0Reis DM, Flach P, Matwin S, Batista G (2016) Fast unsupervised online drift detection using incremental kolmogorov-smirnov test. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1545\u20131554 . Association for Computing Machinery","DOI":"10.1145\/2939672.2939836"},{"key":"3141_CR22","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.patcog.2017.11.009","volume":"76","author":"A Liu","year":"2018","unstructured":"Liu A, Lu J, Liu F, Zhang G (2018) Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recogn 76:256\u2013272","journal-title":"Pattern Recogn"},{"issue":"1","key":"3141_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-024-05890-4","volume":"55","author":"S Agrahari","year":"2025","unstructured":"Agrahari S, Singh AK (2025) Comparison based analysis of window approach for concept drift detection and adaptation. Appl Intell 55(1):1\u201317","journal-title":"Appl Intell"},{"key":"3141_CR24","doi-asserted-by":"crossref","unstructured":"Gama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: Advances in Artificial Intelligence \u2013 SBIA 2004, pp. 286\u2013295 . Springer","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"3141_CR25","unstructured":"Baena-Garc\u0131a M, Campo-\u00c1vila J, Fidalgo R, Bifet A, Gavalda R, Morales-Bueno R (2006) Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, vol. 6, pp. 77\u201386"},{"key":"3141_CR26","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.eswa.2017.08.023","volume":"90","author":"RS Barros","year":"2017","unstructured":"Barros RS, Cabral DR, Gon\u00e7alves PM Jr, Santos SG (2017) Rddm: reactive drift detection method. Expert Syst Appl 90:344\u2013355","journal-title":"Expert Syst Appl"},{"issue":"3","key":"3141_CR27","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/TKDE.2014.2345382","volume":"27","author":"I Frias-Blanco","year":"2014","unstructured":"Frias-Blanco I, Campo-\u00c1vila J, Ramos-Jimenez G, Morales-Bueno R, Ortiz-Diaz A, Caballero-Mota Y (2014) Online and non-parametric drift detection methods based on hoeffding\u2019s bounds. IEEE Trans Knowl Data Eng 27(3):810\u2013823","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"3141_CR28","first-page":"128","volume":"24","author":"K Goel","year":"2021","unstructured":"Goel K, Batra S (2021) Adaptive online learning for classification under concept drift. Int J Comput Sci Eng 24(2):128\u2013135","journal-title":"Int J Comput Sci Eng"},{"key":"3141_CR29","doi-asserted-by":"crossref","unstructured":"Bifet A, Gavalda R (2007) Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443\u2013448 . SIAM","DOI":"10.1137\/1.9781611972771.42"},{"key":"3141_CR30","doi-asserted-by":"crossref","unstructured":"Huang DTJ, Koh YS, Dobbie G, Pears R (2014) Detecting volatility shift in data streams. In: 2014 IEEE International Conference on Data Mining, pp. 863\u2013868 . IEEE","DOI":"10.1109\/ICDM.2014.50"},{"key":"3141_CR31","doi-asserted-by":"publisher","first-page":"115303","DOI":"10.1016\/j.eswa.2021.115303","volume":"183","author":"I Baidari","year":"2021","unstructured":"Baidari I, Honnikoll N (2021) Bhattacharyya distance based concept drift detection method for evolving data stream. Expert Syst Appl 183:115303","journal-title":"Expert Syst Appl"},{"key":"3141_CR32","doi-asserted-by":"publisher","first-page":"114114","DOI":"10.1016\/j.eswa.2020.114114","volume":"167","author":"RTM Chikushi","year":"2021","unstructured":"Chikushi RTM, Barros RSM, Silva MGNM, Maciel BIF (2021) Using spectral entropy and bernoulli map to handle concept drift. Expert Syst Appl 167:114114","journal-title":"Expert Syst Appl"},{"key":"3141_CR33","doi-asserted-by":"crossref","unstructured":"Korycki \u0141, Krawczyk B (2021) Concept drift detection from multi-class imbalanced data streams. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1068\u20131079 . IEEE","DOI":"10.1109\/ICDE51399.2021.00097"},{"issue":"2","key":"3141_CR34","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s13748-021-00230-w","volume":"10","author":"A Agrawal","year":"2021","unstructured":"Agrawal A, Tripathi S, Vardhan M (2021) Active learning approach using a modified least confidence sampling strategy for named entity recognition. Prog Artif Intell 10(2):113\u2013128","journal-title":"Prog Artif Intell"},{"key":"3141_CR35","doi-asserted-by":"publisher","first-page":"106778","DOI":"10.1016\/j.knosys.2021.106778","volume":"215","author":"W Liu","year":"2021","unstructured":"Liu W, Zhang H, Ding Z, Liu Q, Zhu C (2021) A comprehensive active learning method for multiclass imbalanced data streams with concept drift. Knowl-Based Syst 215:106778","journal-title":"Knowl-Based Syst"},{"key":"3141_CR36","doi-asserted-by":"crossref","unstructured":"Zhang H, Chen WW, Rondinelli JM, Chen W et al (2023) Entropy-targeted active learning for bias mitigation in materials data. Appl Phys Rev 10(2)","DOI":"10.1063\/5.0138913"},{"issue":"1","key":"3141_CR37","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TNNLS.2021.3091681","volume":"34","author":"S Liu","year":"2021","unstructured":"Liu S, Xue S, Wu J, Zhou C, Yang J, Li Z, Cao J (2021) Online active learning for drifting data streams. IEEE Trans Neural Netw Learning Syst 34(1):186\u2013200","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"issue":"4","key":"3141_CR38","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/00224065.1986.11979014","volume":"18","author":"JS Hunter","year":"1986","unstructured":"Hunter JS (1986) The exponentially weighted moving average. J Qual Technol 18(4):203\u2013210","journal-title":"J Qual Technol"},{"key":"3141_CR39","doi-asserted-by":"crossref","unstructured":"Pesaranghader A, Viktor HL (2016) Fast hoeffding drift detection method for evolving data streams. In: Machine Learning and Knowledge Discovery in Databases, pp. 96\u2013111 . Springer","DOI":"10.1007\/978-3-319-46227-1_7"},{"key":"3141_CR40","doi-asserted-by":"crossref","unstructured":"Hoeffding W (1994) Probability inequalities for sums of bounded random variables. The collected works of Wassily Hoeffding, 409\u2013426","DOI":"10.1007\/978-1-4612-0865-5_26"},{"key":"3141_CR41","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601\u20131604","journal-title":"J Mach Learn Res"},{"issue":"2","key":"3141_CR42","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2014","unstructured":"Vanschoren J, van Rijn JN, Bischl B, Torgo L (2014) OpenML: networked science in machine learning. ACM SIGKDD Explorations Newsl 15(2):49\u201360","journal-title":"ACM SIGKDD Explorations Newsl"},{"key":"3141_CR43","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s13042-024-02223-2","volume":"16","author":"U Ali","year":"2025","unstructured":"Ali U, Mahmood T (2025) A novel framework for concept drift detection using autoencoders for classification problems in data streams. Int J Mach Learn Cybern 16:397\u2013418","journal-title":"Int J Mach Learn Cybern"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03141-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03141-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03141-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:47:15Z","timestamp":1785502035000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03141-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["3141"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03141-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]},"assertion":[{"value":"14 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"336"}}