{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T06:07:14Z","timestamp":1779516434269,"version":"3.53.1"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":141,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005667","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","doi-asserted-by":"publisher","award":["60\/2024"],"award-info":[{"award-number":["60\/2024"]}],"id":[{"id":"10.13039\/501100005667","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>This work introduces Lean Adaptive XGBoost for Regression (LAX\u2010Reg), a novel, high\u2010efficiency algorithm designed for intelligent processing of nonstationary data streams. The rising volume and velocity of data generated by connected systems necessitate models that can adapt in real time to concept drift. While data stream classification has been extensively studied, data stream regression, particularly using boosting\u2010based methods, remains comparatively underexplored. LAX\u2010Reg builds on XGBoost while avoiding the alternating\u2010model paradigm commonly adopted in existing stream adaptations, which can lead to abrupt performance degradation during model replacement and increased training overhead. Instead, it maintains a single, continuously updated ensemble with bounded complexity through dynamic tree management: outdated trees are selectively removed, remaining trees are updated, and new trees are added using either a FIFO strategy or a Target strategy driven by individual ADWIN drift detectors. The proposed approach is evaluated using prequential evaluation on nine real and synthetic data streams, considering mean squared error (MSE) alongside execution times and memory consumption and compared against five state\u2010of\u2010the\u2010art baselines, including ARF\u2010Reg and AFXGB. Experimental results show that LAX\u2010Reg variants achieve the best average predictive rankings and belong to the leading statistical group according to the Nemenyi post hoc test, while delivering substantial efficiency gains: up to 112\u2009\u00d7\u2009faster execution and 456\u2009\u00d7\u2009lower memory usage compared with ARF\u2010Reg. These results highlight LAX\u2010Reg as a competitive and lightweight solution for adaptive regression in data streams, while also motivating future work on alternative drift detectors, recurring drift scenarios, and the incorporation of temporal features.<\/jats:p>","DOI":"10.1155\/int\/1759600","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T05:44:38Z","timestamp":1779515078000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive XGBoost for Data Stream Regression"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-6720","authenticated-orcid":false,"given":"Julia","family":"Grando","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1033-5907","authenticated-orcid":false,"given":"Yuji Yamada","family":"Correa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6452-1900","authenticated-orcid":false,"given":"Fabiano","family":"Baldo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.21275\/art20203995"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.04.076"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"GamageS.andPremaratneU. 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