{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:58:41Z","timestamp":1775066321410,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031216855","type":"print"},{"value":"9783031216862","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21686-2_7","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T08:30:15Z","timestamp":1668760215000},"page":"92-106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adaptive Fast XGBoost for\u00a0Regression"],"prefix":"10.1007","author":[{"given":"Fernanda Maria","family":"de Souza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Grando","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabiano","family":"Baldo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"issue":"12","key":"7_CR1","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1631\/FITEE.1400398","volume":"16","author":"O Abbaszadeh","year":"2015","unstructured":"Abbaszadeh, O., Amiri, A., Khanteymoori, A.R.: An ensemble method for data stream classification in the presence of concept drift. Front. Inf. Technol. Electron. Eng. 16(12), 1059\u20131068 (2015). https:\/\/doi.org\/10.1631\/FITEE.1400398","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Barddal, J.P.: Vertical and horizontal partitioning in data stream regression ensembles. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE, Curitiba (2019)","DOI":"10.1109\/IJCNN.2019.8852244"},{"key":"7_CR3","unstructured":"Bonassa, G.: Adapta\u00e7\u00e3o de classificador utilizando a biblioteca XGBoost para classifica\u00e7\u00e3o r\u00e1pida de fluxos de dados parcialmente classificados com mudan\u00e7a de conceito (2021)"},{"key":"7_CR4","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., et al.: Xgboost: extreme gradient boosting. R Package Version 0.4-2 1(4), 1\u20134 (2015)"},{"key":"7_CR5","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Ditzler, G., Roveri, M., Alippi, C., Polikar, R.: Learning in nonstationary environments: a survey. Comput. Intell. Mag. 10(4), 12\u201325 (2015).https:\/\/doi.org\/10.1109\/MCI.2015.2471196","DOI":"10.1109\/MCI.2015.2471196"},{"issue":"10","key":"7_CR7","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","volume":"22","author":"R Elwell","year":"2011","unstructured":"Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517\u20131531 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Gama, J., \u017dliobaitundefined, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4) (2014). https:\/\/doi.org\/10.1145\/2523813","DOI":"10.1145\/2523813"},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"Gamage, S., Premaratne, U.: Detecting and adapting to concept drift in continually evolving stochastic processes. In: Proceedings of the International Conference on Big Data and Internet of Thing, BDIOT 2017, pp. 109\u2013114. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3175684.3175723","DOI":"10.1145\/3175684.3175723"},{"key":"7_CR10","unstructured":"Gomes, H.M., Barddal, J.P., Ferreira, L.E.B., Bifet, A.: Adaptive random forests for data stream regression. In: ESANN. IEEE, Curitiba (2018)"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Wo\u017aniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fus. 37, 132\u2013156 (2017)","journal-title":"Inf. Fus."},{"key":"7_CR12","unstructured":"Laney, D.: 3D data management: controlling data volume, velocity, and variety. Technical report, META Group, EUA (2001). http:\/\/blogs.gartner.com\/doug-laney\/files\/2012\/01\/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf"},{"issue":"5","key":"7_CR13","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.ijinfomgt.2016.04.013","volume":"36","author":"D Larson","year":"2016","unstructured":"Larson, D., Chang, V.: A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manag. 36(5), 700\u2013710 (2016)","journal-title":"Int. J. Inf. Manag."},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Liao, Z., Wang, Y.: Rival learner algorithm with drift adaptation for online data stream regression. In: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018, Association for Computing Machinery, New York (2018). https:\/\/doi.org\/10.1145\/3302425.3302475","DOI":"10.1145\/3302425.3302475"},{"key":"7_CR15","unstructured":"Lopes, R.H., Reid, I., Hobson, P.R.: The two-dimensional kolmogorov-smirnov test (2007)"},{"issue":"12","key":"7_CR16","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"7_CR17","doi-asserted-by":"publisher","first-page":"606","DOI":"10.3390\/app10020606","volume":"10","author":"OA Mahdi","year":"2020","unstructured":"Mahdi, O.A., Pardede, E., Ali, N., Cao, J.: Fast reaction to sudden concept drift in the absence of class labels. Appl. Sci. 10(2), 606 (2020)","journal-title":"Appl. Sci."},{"issue":"06","key":"7_CR18","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3414\/ME13-01-0122","volume":"53","author":"A Mayr","year":"2014","unstructured":"Mayr, A., Binder, H., Gefeller, O., Schmid, M.: The evolution of boosting algorithms. Methods Inf. Med. 53(06), 419\u2013427 (2014)","journal-title":"Methods Inf. Med."},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"349","DOI":"10.3390\/smartcities4010021","volume":"4","author":"H Mehmood","year":"2021","unstructured":"Mehmood, H., Kostakos, P., Cortes, M., Anagnostopoulos, T., Pirttikangas, S., Gilman, E.: Concept drift adaptation techniques in distributed environment for real-world data streams. Smart Cities 4(1), 349\u2013371 (2021)","journal-title":"Smart Cities"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Montiel, J., Mitchell, R., Frank, E., Pfahringer, B., Abdessalem, T., Bifet, A.: Adaptive XGBoost for evolving data streams. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE, Hamilton (2020)","DOI":"10.1109\/IJCNN48605.2020.9207555"},{"key":"7_CR21","unstructured":"Montiel, J., Read, J., Bifet, A., Abdessalem, T.: Scikit-multiflow: a multi-output streaming framework. J. Mach. Learn. Res. 19(72), 1\u20135 (2018). http:\/\/jmlr.org\/papers\/v19\/18-251.html"},{"key":"7_CR22","first-page":"651","volume":"9","author":"S Ramraj","year":"2016","unstructured":"Ramraj, S., Uzir, N., Sunil, R., Banerjee, S.: Experimenting XGBoost algorithm for prediction and classification of different datasets. Int. J. Control Theory Appl. 9, 651\u2013662 (2016)","journal-title":"Int. J. Control Theory Appl."},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Schapire, R.E.: The boosting approach to machine learning: an overview. In: Nonlinear Estimation and Classification, pp. 149\u2013171 (2003)","DOI":"10.1007\/978-0-387-21579-2_9"},{"issue":"4","key":"7_CR24","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.icte.2020.05.011","volume":"6","author":"MMW Yan","year":"2020","unstructured":"Yan, M.M.W.: Accurate detecting concept drift in evolving data streams. ICT Express 6(4), 332\u2013338 (2020)","journal-title":"ICT Express"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Yang, L., Manias, D.M., Shami, A.: Pwpae: an ensemble framework for concept drift adaptation in iot data streams. arXiv preprint arXiv:2109.05013 (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9685338"},{"key":"7_CR26","doi-asserted-by":"publisher","unstructured":"Yu, H., Lu, J., Zhang, G.: Morstreaming: a multioutput regression system for streaming data. IEEE Trans. Syst. Man Cybern. Syst., 1\u201313 (2021). https:\/\/doi.org\/10.1109\/TSMC.2021.3102978","DOI":"10.1109\/TSMC.2021.3102978"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21686-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:08:59Z","timestamp":1709831339000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21686-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216855","9783031216862"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21686-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Campinas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www2.sbc.org.br\/bracis2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"225","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"89","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}