{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:44:19Z","timestamp":1759970659921,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176107","62076111","62076215"],"award-info":[{"award-number":["62176107","62076111","62076215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Learning from a nonstationary data stream is challenging, as a data stream is generally considered to be endless, and the learning model is required to be constantly amended for adapting the shifting data distributions. When it meets multi-label data, the challenge would be further intensified. In this study, an adaptive online weighted multi-label ensemble learning algorithm called MLDME (multi-label learning with distribution matching ensemble) is proposed. It simultaneously calculates both the feature matching level and label matching level between any one reserved data block and the new received data block, further providing an adaptive decision weight assignment for ensemble classifiers based on their distribution similarities. Specifically, MLDME abandons the most commonly used but not totally correct underlying hypothesis that in a data stream, each data block always has the most approximate distribution with that emerging after it; thus, MLDME could provide a just-in-time decision for the new received data block. In addition, to avoid an infinite extension of ensemble classifiers, we use a fixed-size buffer to store them and design three different dynamic classifier updating rules. Experimental results for nine synthetic and three real-world multi-label nonstationary data streams indicate that the proposed MLDME algorithm is superior to some popular and state-of-the-art online learning paradigms and algorithms, including two specifically designed ones for classifying a nonstationary multi-label data stream.<\/jats:p>","DOI":"10.3390\/sym17020182","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T11:50:47Z","timestamp":1737719447000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Label Learning with Distribution Matching Ensemble: An Adaptive and Just-In-Time Weighted Ensemble Learning Algorithm for Classifying a Nonstationary Online Multi-Label Data Stream"],"prefix":"10.3390","volume":"17","author":[{"given":"Chao","family":"Shen","sequence":"first","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}]},{"given":"Bingyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-2727","authenticated-orcid":false,"given":"Changbin","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}]},{"given":"Xibei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}]},{"given":"Sen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Yancheng Institute of Technology, Yancheng 224051, China"}]},{"given":"Changming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ethnic language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9621-4158","authenticated-orcid":false,"given":"Hualong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1109\/TKDE.2016.2563424","article-title":"Online learning from trapezoidal data streams","volume":"28","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","first-page":"2400","article-title":"Novelty detection and online learning for chunk data streams","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1007\/s10618-015-0448-4","article-title":"Characterizing concept drift","volume":"30","author":"Webb","year":"2016","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_4","first-page":"2346","article-title":"Learning under concept drift: A review","volume":"31","author":"Lu","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s10618-018-0554-1","article-title":"Analyzing concept drift and shift from sample data","volume":"32","author":"Webb","year":"2018","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3158645","article-title":"Activity recognition with evolving data streams: A review","volume":"51","author":"Abdallah","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108885","DOI":"10.1016\/j.patcog.2022.108885","article-title":"Incremental learning from low-labelled stream data in open-set video face recognition","volume":"131","author":"Pardo","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_8","first-page":"1","article-title":"A survey on stream-based recommender systems","volume":"54","author":"Abdessalem","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/JSYST.2013.2294120","article-title":"Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid: A feasibility study","volume":"9","author":"Faisal","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1002\/tee.23307","article-title":"On-Line Fault Diagnosis Model of Distribution Transformer Based on Parallel Big Data Stream and Transfer Learning","volume":"18","author":"Yang","year":"2023","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.asoc.2017.08.008","article-title":"Using intelligent computing and data stream mining for behavioral finance associated with market profile and financial physics","volume":"68","author":"Lin","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/TFUZZ.2013.2254492","article-title":"A fuzzy model with online incremental SVM and margin-selective gradient descent learning for classification problems","volume":"22","author":"Cheng","year":"2013","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111636","DOI":"10.1016\/j.knosys.2024.111636","article-title":"Adaptive tree-like neural network: Overcoming catastrophic forgetting to classify streaming data with concept drifts","volume":"293","author":"Wen","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107378","DOI":"10.1016\/j.asoc.2021.107378","article-title":"Online ensemble learning algorithm for imbalanced data stream","volume":"107","author":"Du","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3054925","article-title":"A survey on ensemble learning for data stream classification","volume":"50","author":"Gomes","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104983","DOI":"10.1016\/j.knosys.2019.104983","article-title":"A heterogeneous online learning ensemble for non-stationary environments","volume":"188","author":"Idrees","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neucom.2018.11.098","article-title":"Adaptive online extreme learning machine by regulating forgetting factor by concept drift map","volume":"343","author":"Yu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"66408","DOI":"10.1109\/ACCESS.2021.3076264","article-title":"ElStream: An ensemble learning approach for concept drift detection in dynamic social big data stream learning","volume":"9","author":"Abbasi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.inffus.2019.03.006","article-title":"An overview and comprehensive comparison of ensembles for concept drift","volume":"52","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1109\/TNNLS.2022.3183120","article-title":"Dynamic ensemble selection for imbalanced data streams with concept drift","volume":"35","author":"Jiao","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1007\/s10462-022-10232-2","article-title":"Unsupervised concept drift detection for multi-label data streams","volume":"56","author":"Gulcan","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"120578","DOI":"10.1109\/ACCESS.2022.3222178","article-title":"DME: An adaptive and just-in-time weighted ensemble learning method for classifying block-based concept drift steam","volume":"10","author":"Feng","year":"2022","journal-title":"IEEE Access"},{"key":"ref_23","first-page":"659","article-title":"Gaussian mixture models","volume":"741","author":"Reynolds","year":"2009","journal-title":"Encycl. Biom."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Adler, A., Tang, J., and Polyanskiy, Y. (2021, January 12\u201320). Quantization of Random Distributions Under KL Divergence. Proceedings of the 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia.","DOI":"10.1109\/ISIT45174.2021.9518081"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Domingos, P., and Hulten, G. (2000, January 1). Mining high-speed data streams. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, WA, USA.","DOI":"10.1145\/347090.347107"},{"key":"ref_26","first-page":"2755","article-title":"Dynamic weighted majority: An ensemble method for drifting concepts","volume":"8","author":"Kolter","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/TNNLS.2013.2251352","article-title":"Reacting to different types of concept drift: The accuracy updated ensemble algorithm","volume":"25","author":"Brzezinski","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1109\/TKDE.2017.2785795","article-title":"Multi-label learning with global and local label correlation","volume":"30","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.patcog.2019.01.007","article-title":"Multi-label classification via label correlation and first order feature dependance in a data stream","volume":"90","author":"Nguyen","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121366","DOI":"10.1016\/j.eswa.2023.121366","article-title":"CC++: An algorithm family based on ensemble of classifier chains for classifying imbalanced multi-label data","volume":"236","author":"Duan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107506","DOI":"10.1016\/j.engappai.2023.107506","article-title":"A partition-based problem transformation algorithm for classifying imbalanced multi-label data","volume":"128","author":"Duan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3363573","article-title":"Multi-label punitive kNN with self-adjusting memory for drifting data streams","volume":"13","author":"Roseberry","year":"2019","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2021.02.032","article-title":"Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams","volume":"442","author":"Roseberry","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hershey, J.R., and Olsen, P.A. (2007, January 15\u201320). Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models. Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP\u201907, Honolulu, HI, USA.","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_36","first-page":"1","article-title":"Scikit-multiflow: A multi-output streaming framework","volume":"19","author":"Montiel","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28488","DOI":"10.1109\/ACCESS.2018.2839340","article-title":"LW-ELM: A fast and flexible cost-sensitive learning framework for classifying imbalanced data","volume":"6","author":"Yu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_39","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","article-title":"Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power","volume":"180","author":"Luengo","year":"2010","journal-title":"Inf. Sci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:53Z","timestamp":1759919753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["sym17020182"],"URL":"https:\/\/doi.org\/10.3390\/sym17020182","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}