{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:33:12Z","timestamp":1758043992652,"version":"3.44.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100019290","name":"Halmstad University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100019290","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Gradient boosting has been extensively studied in batch learning. Recently, its streaming adaptation, Streaming Gradient Boosted Trees (<jats:sc>Sgbt<\/jats:sc>), has surpassed existing state-of-the-art random subspace and random patches methods for streaming classification under various drift scenarios. However, its application in streaming regression remains unexplored. Vanilla <jats:sc>Sgbt<\/jats:sc> with squared loss exhibits high variance when applied to streaming regression problems. To address this, we utilize bagging streaming regressors in this work to create Streaming Gradient Boosted Regression (<jats:sc>Sgbr<\/jats:sc>). Bagging streaming regressors are employed in two ways: first, as base learners within the existing <jats:sc>Sgbt<\/jats:sc> framework, and second, as an ensemble method that aggregates multiple <jats:sc>Sgbt<\/jats:sc>s. Our extensive experiments on 11 streaming regression datasets, encompassing multiple drift scenarios, demonstrate that the <jats:sc>Sgb(Oza)<\/jats:sc>, a variant of the first <jats:sc>Sgbr<\/jats:sc> category, significantly outperforms current state-of-the-art streaming regression methods in terms of both predictive power and computational cost.<\/jats:p>","DOI":"10.1007\/s10618-025-01147-x","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:09:00Z","timestamp":1754474940000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gradient boosted bagging for evolving data stream regression"],"prefix":"10.1007","volume":"39","author":[{"given":"Nuwan","family":"Gunasekara","sequence":"first","affiliation":[]},{"given":"Bernhard","family":"Pfahringer","sequence":"additional","affiliation":[]},{"given":"Heitor Murilo","family":"Gomes","sequence":"additional","affiliation":[]},{"given":"Albert","family":"Bifet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"1147_CR1","doi-asserted-by":"crossref","unstructured":"Bifet A, Gavalda R (2007) Learning from time-changing data with adaptive windowing. In: SIAM (SDM), pp. 443\u2013448. SIAM","DOI":"10.1137\/1.9781611972771.42"},{"key":"1147_CR2","doi-asserted-by":"crossref","unstructured":"Bifet A, Gavald\u00e0 R, Holmes G, Pfahringer B (2018) Machine Learning for Data Streams: with Practical Examples in MOA. MIT Press","DOI":"10.7551\/mitpress\/10654.001.0001"},{"key":"1147_CR3","unstructured":"B\u00fchlmann P, Yu B (2000) Explaining bagging. In: Research Report\/Seminar F\u00fcr Statistik, Eidgen\u00f6ssische Technische Hochschule (ETH), vol. 92. Seminar f\u00fcr Statistik, Eidgen\u00f6ssische Technische Hochschule (ETH)"},{"key":"1147_CR4","unstructured":"Chen S-T, Lin H-T, Lu C-J (2012) An online boosting algorithm with theoretical justifications. arXiv preprint arXiv:1206.6422"},{"key":"1147_CR5","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"1147_CR6","doi-asserted-by":"publisher","first-page":"623","DOI":"10.7717\/peerj-cs.623","volume":"7","author":"D Chicco","year":"2021","unstructured":"Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation. Peerj computer science 7:623","journal-title":"Peerj computer science"},{"key":"1147_CR7","doi-asserted-by":"crossref","unstructured":"Choudhary A, Jha P, Tiwari A, Bharill N (2021) A brief survey on concept drifted data stream regression. SocProS, 733\u2013744","DOI":"10.1007\/978-981-16-2712-5_57"},{"key":"1147_CR8","first-page":"564","volume":"2000","author":"P Domingos","year":"2000","unstructured":"Domingos P (2000) A unified bias-variance decomposition for zero-one and squared loss. AAAI\/IAAI 2000:564\u2013569","journal-title":"AAAI\/IAAI"},{"key":"1147_CR9","doi-asserted-by":"crossref","unstructured":"Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71\u201380","DOI":"10.1145\/347090.347107"},{"key":"1147_CR10","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s13748-013-0040-3","volume":"2","author":"H Fanaee-T","year":"2014","unstructured":"Fanaee-T H, Gama J (2014) Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence 2:113\u2013127","journal-title":"Progress in Artificial Intelligence"},{"issue":"1","key":"1147_CR11","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"1147_CR12","doi-asserted-by":"crossref","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189\u20131232","DOI":"10.1214\/aos\/1013203451"},{"issue":"1","key":"1147_CR13","first-page":"1","volume":"19","author":"JH Friedman","year":"1991","unstructured":"Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1\u201367","journal-title":"Ann Stat"},{"issue":"4","key":"1147_CR14","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367\u2013378","journal-title":"Comput Stat Data Anal"},{"issue":"2","key":"1147_CR15","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337\u2013407","journal-title":"Ann Stat"},{"key":"1147_CR16","unstructured":"Gomes HM, Barddal JP, Ferreira LEB, Bifet A (2018) Adaptive random forests for data stream regression. In: ESANN"},{"key":"1147_CR17","doi-asserted-by":"crossref","unstructured":"Gomes HM, Montiel J, Mastelini SM, Pfahringer B, Bifet A (2020) On ensemble techniques for data stream regression. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE","DOI":"10.1109\/IJCNN48605.2020.9206756"},{"key":"1147_CR18","doi-asserted-by":"crossref","unstructured":"Gomes HM, Read J, Bifet A (2019) Streaming random patches for evolving data stream classification. In: IEEE (ICDM), pp. 240\u2013249. IEEE","DOI":"10.1109\/ICDM.2019.00034"},{"issue":"9","key":"1147_CR19","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, Barddal JP, Enembreck F, Pfahringer B, Holmes G, Abdessalem T (2017) Adaptive random forests for evolving data stream classification. Mach Learn 106(9):1469\u20131495","journal-title":"Mach Learn"},{"issue":"2","key":"1147_CR20","first-page":"1","volume":"50","author":"HM Gomes","year":"2017","unstructured":"Gomes HM, Barddal JP, Enembreck F, Bifet A (2017) A survey on ensemble learning for data stream classification. ACM (CSUR) 50(2):1\u201336","journal-title":"ACM (CSUR)"},{"key":"1147_CR21","unstructured":"Gouk H, Pfahringer B, Frank E (2019) Stochastic gradient trees. In: Lee, W.S., Suzuki, T. (eds.) Proceedings of The Eleventh Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 101, pp. 1094\u20131109. PMLR. https:\/\/proceedings.mlr.press\/v101\/gouk19a.html"},{"key":"1147_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-024-06517-y","author":"N Gunasekara","year":"2024","unstructured":"Gunasekara N, Pfahringer B, Gomes H, Bifet A (2024) Gradient boosted trees for evolving data streams. Mach Learn. https:\/\/doi.org\/10.1007\/s10994-024-06517-y","journal-title":"Mach Learn"},{"key":"1147_CR23","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-031-91398-3_27","volume-title":"Advances in Intelligent Data Analysis XXIII","author":"N Gunasekara","year":"2025","unstructured":"Gunasekara N, Nowaczyk S, Pashami S (2025) Pragmatic paradigm for multi-stream regression. In: Krempl G, Puolam\u00e4ki K, Miliou I (eds) Advances in Intelligent Data Analysis XXIII. Springer, Cham, pp 358\u2013372"},{"key":"1147_CR24","doi-asserted-by":"crossref","unstructured":"Halstead B, Koh YS, Riddle P, Pears R, Pechenizkiy M, Bifet A, Olivares G, Coulson G (2022) Analyzing and repairing concept drift adaptation in data stream classification. Machine Learning, 3489\u20133523","DOI":"10.1007\/s10994-021-05993-w"},{"key":"1147_CR25","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.commatsci.2018.07.052","volume":"154","author":"K Hamidieh","year":"2018","unstructured":"Hamidieh K (2018) A data-driven statistical model for predicting the critical temperature of a superconductor. Comput Mater Sci 154:346\u2013354","journal-title":"Comput Mater Sci"},{"key":"1147_CR26","doi-asserted-by":"crossref","unstructured":"Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: ACM SIGKDD, pp. 97\u2013106","DOI":"10.1145\/502512.502529"},{"issue":"1","key":"1147_CR27","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/s10618-010-0201-y","volume":"23","author":"E Ikonomovska","year":"2011","unstructured":"Ikonomovska E, Gama J, D\u017eeroski S (2011) Learning model trees from evolving data streams. Data Min Knowl Discov 23(1):128\u2013168","journal-title":"Data Min Knowl Discov"},{"key":"1147_CR28","doi-asserted-by":"crossref","unstructured":"Kovacs A, Bogdandy B, Toth Z (2021) Predict stock market prices with recurrent neural networks using nasdaq data stream. In: 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 000449\u2013000454. IEEE","DOI":"10.1109\/SACI51354.2021.9465634"},{"issue":"3","key":"1147_CR29","doi-asserted-by":"publisher","first-page":"740","DOI":"10.3390\/en13030740","volume":"13","author":"JL Lobo","year":"2020","unstructured":"Lobo JL, Ballesteros I, Oregi I, Del Ser J, Salcedo-Sanz S (2020) Stream learning in energy iot systems: a case study in combined cycle power plants. Energies 13(3):740","journal-title":"Energies"},{"key":"1147_CR30","doi-asserted-by":"crossref","unstructured":"Manapragada C, Webb GI, Salehi M (2018) Extremely fast decision tree. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1953\u20131962","DOI":"10.1145\/3219819.3220005"},{"issue":"10","key":"1147_CR31","doi-asserted-by":"publisher","first-page":"6755","DOI":"10.1109\/TNNLS.2022.3212859","volume":"34","author":"SM Mastelini","year":"2022","unstructured":"Mastelini SM, Nakano FK, Vens C, Leon Ferreira ACP et al (2022) Online extra trees regressor. IEEE Transactions on Neural Networks and Learning Systems 34(10):6755\u20136767","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1147_CR32","doi-asserted-by":"crossref","unstructured":"Montiel J, Mitchell R, Frank E, Pfahringer B, Abdessalem T, Bifet A (2020) Adaptive xgboost for evolving data streams. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE","DOI":"10.1109\/IJCNN48605.2020.9207555"},{"key":"1147_CR33","doi-asserted-by":"publisher","unstructured":"Nash W, Sellers T, Talbot S, Cawthorn A, Ford W (1995) Abalone. UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C55C7W","DOI":"10.24432\/C55C7W"},{"key":"1147_CR34","doi-asserted-by":"publisher","first-page":"13050","DOI":"10.1109\/ACCESS.2019.2892902","volume":"7","author":"SV Oprea","year":"2019","unstructured":"Oprea SV, B\u00e2ra A, Diaconita V (2019) Sliding time window electricity consumption optimization algorithm for communities in the context of big data processing. IEEE Access 7:13050\u201313067","journal-title":"IEEE Access"},{"key":"1147_CR35","unstructured":"Oza NC, Russell SJ (2001) Online bagging and boosting. In: International Workshop on Artificial Intelligence and Statistics, pp. 229\u2013236. PMLR"},{"key":"1147_CR36","unstructured":"S ARV. Demand Forecasting \u2014 kaggle.com. https:\/\/www.kaggle.com\/datasets\/aswathrao\/demand-forecasting. [Accessed 31-10-2024]"},{"issue":"5","key":"1147_CR37","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.1007\/s10618-022-00858-9","volume":"36","author":"Y Sun","year":"2022","unstructured":"Sun Y, Pfahringer B, Gomes HM, Bifet A (2022) Soknl: A novel way of integrating k-nearest neighbours with adaptive random forest regression for data streams. Data Min Knowl Discov 36(5):2006\u20132032","journal-title":"Data Min Knowl Discov"},{"key":"1147_CR38","doi-asserted-by":"crossref","unstructured":"Sun Y, Gomes HM, Pfahringer B, Bifet A (2024) Real-time energy pricing in new zealand: an evolving stream analysis. In: Pacific Rim International Conference on Artificial Intelligence, pp. 91\u201397. Springer","DOI":"10.1007\/978-981-96-0128-8_8"},{"key":"1147_CR39","unstructured":"Sun Y, Gomes HM, Pfahringer B, Bifet A (2025) Evaluation for regression analyses on evolving data streams. arXiv preprint arXiv:2502.07213"},{"key":"1147_CR40","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.neucom.2022.03.038","volume":"491","author":"K Wang","year":"2022","unstructured":"Wang K, Lu J, Liu A, Song Y, Xiong L, Zhang G (2022) Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing 491:288\u2013304","journal-title":"Neurocomputing"},{"issue":"7","key":"1147_CR41","first-page":"557","volume":"20","author":"S Wright","year":"1921","unstructured":"Wright S (1921) Correlation and causation. J Agric Res 20(7):557","journal-title":"J Agric Res"},{"key":"1147_CR42","doi-asserted-by":"crossref","unstructured":"Wu H, Elizaveta D, Zhadan A, Petrosian O (2023) Forecasting online adaptation methods for energy domain. Eng Appl Artif Intell, 106499","DOI":"10.1016\/j.engappai.2023.106499"},{"issue":"2","key":"1147_CR43","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/00220970109600656","volume":"69","author":"P Yin","year":"2001","unstructured":"Yin P, Fan X (2001) Estimating r 2 shrinkage in multiple regression: A comparison of different analytical methods. J Exp Educ 69(2):203\u2013224","journal-title":"J Exp Educ"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01147-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-025-01147-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01147-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T10:29:22Z","timestamp":1757672962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-025-01147-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":43,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["1147"],"URL":"https:\/\/doi.org\/10.1007\/s10618-025-01147-x","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"assertion":[{"value":"8 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"65"}}