{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:48:36Z","timestamp":1780498116305,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2017\/27\/B\/ST6\/01325"],"award-info":[{"award-number":["2017\/27\/B\/ST6\/01325"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important. This task is hindered by changing the distribution of data over time, called the concept drift, and the emergence of a problem of disproportion between classes\u2014such as in the detection of network attacks or fraud detection problems. In the following work, we propose methods to modify existing stream processing solutions\u2014Accuracy Weighted Ensemble (AWE) and Accuracy Updated Ensemble (AUE), which have demonstrated their effectiveness in adapting to time-varying class distribution. The introduced changes are aimed at increasing their quality on binary classification of imbalanced data. The proposed modifications contain the inclusion of aggregate metrics, such as F1-score, G-mean and balanced accuracy score in calculation of the member classifiers weights, which affects their composition and final prediction. Moreover, the impact of data sampling on the algorithm\u2019s effectiveness was also checked. Complex experiments were conducted to define the most promising modification type, as well as to compare proposed methods with existing solutions. Experimental evaluation shows an improvement in the quality of classification compared to the underlying algorithms and other solutions for processing imbalanced data streams.<\/jats:p>","DOI":"10.3390\/e22080849","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T03:16:46Z","timestamp":1596424606000},"page":"849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9339-2669","authenticated-orcid":false,"given":"Weronika","family":"Wegier","sequence":"first","affiliation":[{"name":"Department of Systems and Computer Networks, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9578-8395","authenticated-orcid":false,"given":"Pawel","family":"Ksieniewicz","sequence":"additional","affiliation":[{"name":"Department of Systems and Computer Networks, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","article-title":"Ensemble learning for data stream analysis: A survey","volume":"37","author":"Krawczyk","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_2","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. (CSUR)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.aci.2014.10.001","article-title":"Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method","volume":"12","author":"Adeniyi","year":"2016","journal-title":"Appl. Comput. Inform."},{"key":"ref_4","unstructured":"CISCO (2018, December 15). Cisco Visual Networking Index: Forecast and Trends, 2017\u20132022. Available online: https:\/\/www.cisco.com\/c\/dam\/m\/en_us\/network-intelligence\/service-provider\/digital-transformation\/knowledge-network-webinars\/pdfs\/1213-business-services-ckn.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1016\/j.eswa.2014.02.026","article-title":"Learned lessons in credit card fraud detection from a practitioner perspective","volume":"41","author":"Caelen","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yuan, X., Li, C., and Li, X. (2017, January 29\u201331). DeepDefense: Identifying DDoS attack via deep learning. Proceedings of the 2017 IEEE International Conference on Smart Computing (SMARTCOMP), Hong Kong, China.","DOI":"10.1109\/SMARTCOMP.2017.7946998"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: Open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jeni, L.A., Cohn, J.F., and De La Torre, F. (2013, January 2\u20135). Facing imbalanced data\u2013recommendations for the use of performance metrics. Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland.","DOI":"10.1109\/ACII.2013.47"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patrec.2008.08.010","article-title":"An experimental comparison of performance measures for classification","volume":"30","author":"Ferri","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Babcock, B., Babu, S., Datar, M., Motwani, R., and Widom, J. (2002, January 3\u20135). Models and issues in data stream systems. Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Madison, WI, USA.","DOI":"10.1145\/543613.543615"},{"key":"ref_12","first-page":"58","article-title":"The problem of concept drift: Definitions and related work","volume":"106","author":"Tsymbal","year":"2004","journal-title":"Comput. Sci. Dep. Trinity Coll. Dublin"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wo\u017aniak, M., Kasprzak, A., and Cal, P. (2013, January 18\u201320). Weighted aging classifier ensemble for the incremental drifted data streams. Proceedings of the International Conference on Flexible Query Answering Systems, Granada, Spain.","DOI":"10.1007\/978-3-642-40769-7_50"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gama, J., Medas, P., Castillo, G., and Rodrigues, P. (Germany 2004). Learning with drift detection. Brazilian Symposium on Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1340001","DOI":"10.1142\/S1469026813400014","article-title":"Online class imbalance learning and its applications in fault detection","volume":"12","author":"Wang","year":"2013","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_16","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":"J. Artif. Intell. Res."},{"key":"ref_17","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE world Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Laurikkala, J. (2001, January 1\u20134). Improving identification of difficult small classes by balancing class distribution. Proceedings of the Conference on Artificial Intelligence in Medicine in Europe, Cascais, Portugal.","DOI":"10.1007\/3-540-48229-6_9"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s10044-006-0043-9","article-title":"Two-stage binary classifier with fuzzy-valued loss function","volume":"9","author":"Burduk","year":"2006","journal-title":"Pattern Anal. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1007\/s00500-014-1492-5","article-title":"One-class classifiers with incremental learning and forgetting for data streams with concept drift","volume":"19","author":"Krawczyk","year":"2015","journal-title":"Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zyblewski, P., Ksieniewicz, P., and Wo\u017aniak, M. (2019, January 16\u201320). Classifier selection for highly imbalanced data streams with minority driven ensemble. Proceedings of the International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland.","DOI":"10.1007\/978-3-030-20912-4_57"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, H., Fan, W., Yu, P.S., and Han, J. (2003, January 24\u201327). Mining concept-drifting data streams using ensemble classifiers. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, Washington, DC, USA.","DOI":"10.1145\/956750.956778"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Brzezi\u0144ski, D., and Stefanowski, J. (2011, January 23\u201325). Accuracy updated ensemble for data streams with concept drift. Proceedings of the International Conference On Hybrid Artificial Intelligence Systems, Wroclaw, Poland.","DOI":"10.1007\/978-3-642-21222-2_19"},{"key":"ref_24","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_25","unstructured":"Spyromitros-Xioufis, E., Spiliopoulou, M., Tsoumakas, G., and Vlahavas, I. (2011, January 16\u201322). Dealing with concept drift and class imbalance in multi-label stream classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., and Buhmann, J.M. (2010, January 23\u201326). The balanced accuracy and its posterior distribution. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.764"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chinchor, N. (1992, January 16\u201318). MUC-4 Evaluation Metrics. Proceedings of the 4th Conference on Message Understanding (MUC4\u201992), McLean, VA, USA.","DOI":"10.3115\/1072064.1072067"},{"key":"ref_28","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: One-sided selection","volume":"Volume 97","author":"Kubat","year":"1997","journal-title":"Proc. 14th International Conference on Machine Learning"},{"key":"ref_29","unstructured":"Guyon, I. (2003, January 11\u201313). Design of experiments of the NIPS 2003 variable selection benchmark. Proceedings of the NIPS 2003 Workshop on Feature Extraction And Feature Selection, Whistler, BC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/biomet\/52.1-2.203","article-title":"A generalized Wilcoxon test for comparing arbitrarily singly-censored samples","volume":"52","author":"Gehan","year":"1965","journal-title":"Biometrika"},{"key":"ref_31","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","unstructured":"Ksieniewicz, P., and Zyblewski, P. (2020). stream-learn\u2013open-source Python library for difficult data stream batch analysis. arXiv."},{"key":"ref_33","first-page":"1","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","first-page":"2914","article-title":"Scikit-Multiflow: A Multi-output Streaming Framework","volume":"19","author":"Montiel","year":"2018","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/8\/849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:53:17Z","timestamp":1760176397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/8\/849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,31]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["e22080849"],"URL":"https:\/\/doi.org\/10.3390\/e22080849","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,31]]}}}