{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T16:19:28Z","timestamp":1746807568883,"version":"3.40.5"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["http:\/\/dx.doi.org\/10.13039\/501100000038"],"award-info":[{"award-number":["http:\/\/dx.doi.org\/10.13039\/501100000038"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000196","name":"Canada Foundation for Innovation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000196","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":[[2024,3]]},"DOI":"10.1007\/s10618-023-00970-4","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T09:02:20Z","timestamp":1697533340000},"page":"569-596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mondrian forest for data stream classification under memory constraints"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2129-5517","authenticated-orcid":false,"given":"Martin","family":"Khannouz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tristan","family":"Glatard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"issue":"22","key":"970_CR1","doi-asserted-by":"publisher","first-page":"5026","DOI":"10.3390\/s19225026","volume":"19","author":"D Akbar","year":"2019","unstructured":"Akbar D, Omid S, Tristan G, Emad Shihab (2019) A quantitative comparison of overlapping and non-overlapping sliding windows for human activity recognition using inertial sensors. Sensors 19(22):5026","journal-title":"Sensors"},{"key":"970_CR2","doi-asserted-by":"crossref","unstructured":"Albert B, Ricard G (2009) Adaptive learning from evolving data streams. Advances in Intelligent Data Analysis VIII. Springer, Berlin Heidelberg, pp 249\u2013260","DOI":"10.1007\/978-3-642-03915-7_22"},{"key":"970_CR3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10994-019-05840-z","volume":"109","author":"C Alberto","year":"2020","unstructured":"Alberto C, Bartosz K (2020) Kappa updated ensemble for drifting data stream mining. Mach Learning 109:175\u2013218","journal-title":"Mach Learning"},{"key":"970_CR4","first-page":"249","volume-title":"Adaptive learning from evolving data streams","author":"A Bifet","year":"2009","unstructured":"Albert B, Ricard G (2009) Adaptive learning from evolving data streams. Advances in intelligent data analysis VIII. Springer, Berlin Heidelberg, pp 249\u2013260"},{"issue":"May","key":"970_CR5","first-page":"1601","volume":"11","author":"Albert Bifet","year":"2010","unstructured":"Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601\u20131604","journal-title":"J Mach Learn Res"},{"key":"970_CR6","doi-asserted-by":"crossref","unstructured":"Bifet Albert, Gavald\u00e0 Ricard (apr 2007) Learning from Time-Changing Data with Adaptive Windowing. In: Proceedings of the 2007 SIAM international conference on data mining. society for industrial and applied mathematics","DOI":"10.1137\/1.9781611972771.42"},{"key":"970_CR7","doi-asserted-by":"crossref","unstructured":"Bifet A, Holmes G, Pfahringer B, Kirkby R, Gavald\u00e0 R (2009) New ensemble methods for evolving data streams. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD \u201909. ACM Press","DOI":"10.1145\/1557019.1557041"},{"key":"970_CR8","doi-asserted-by":"crossref","unstructured":"Bifet A, Zhang J, Fan W, He C, Zhang J, Qian J, Holmes G, Pfahringer B (2017) Extremely Fast Decision Tree Mining for Evolving Data Streams. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining - KDD \u201917, pp 1733\u20131742,","DOI":"10.1145\/3097983.3098139"},{"issue":"7","key":"970_CR9","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1007\/s10994-022-06168-x","volume":"111","author":"A Cano","year":"2022","unstructured":"Cano A, Krawczyk B (2022) ROSE: Robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Mach Learn 111(7):2561\u20132599","journal-title":"Mach Learn"},{"key":"970_CR10","unstructured":"Dan MT, Scott S, Andrew G, and Ilya K (2014) . Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises, RecoFit"},{"key":"970_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100461","volume":"16","author":"L Dutta","year":"2021","unstructured":"Dutta L, Bharali S (2021) Tinyml meets iot: a comprehensive survey. Internet of Things 16:100461","journal-title":"Internet of Things"},{"key":"970_CR12","unstructured":"Elbasi S, B\u00fcy\u00fck\u00e7ak\u0131, Bonab H, Can F (2021) On-the-fly ensemble pruning in evolving data streams"},{"key":"970_CR13","doi-asserted-by":"crossref","unstructured":"Gama J, Sebasti\u00e3o R, Rodrigues P\u00a0P (2009) Issues in Evaluation of Stream Learning Algorithms. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 329-338","DOI":"10.1145\/1557019.1557060"},{"key":"970_CR14","unstructured":"Gupta C, Suggala A\u00a0S, Goyal A, Simhadri H\u00a0V, Paranjape B, Kumar A, Goyal S, Udupa R, Varma M, Jain P (06\u201311 Aug 2017) ProtoNN: Compressed and accurate kNN for resource-scarce devices. In Doina Precup and Yee\u00a0Whye Teh, editors, In: Proceedings of the 34th International Conference on Machine Learning, volume\u00a070 of Proceedings of Machine Learning Research, pp 1331\u20131340. PMLR"},{"key":"970_CR15","unstructured":"HelloAlone. C++ implementation of the mondrian forest, (2018)"},{"issue":"1","key":"970_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/comjnl\/bxab138","volume":"66","author":"N Honnikoll","year":"2021","unstructured":"Honnikoll N, Baidari I (2021) Mean error rate weighted online boosting method. The Comput J 66(1):1\u201315","journal-title":"The Comput J"},{"key":"970_CR17","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.21105\/joss.01485","volume":"4","author":"M Khannouz","year":"2019","unstructured":"Khannouz M, Li B, Glatard T (2019) OrpailleCC: a library for data stream analysis on embedded systems. The J Open Source Softw 4:1485","journal-title":"The J Open Source Softw"},{"key":"970_CR18","unstructured":"Kumar A, Goyal S, Varma M (2017) Resource-efficient machine learning in 2 kb ram for the internet of things. In: Proceedings of the 34th international conference on machine learning - volume 70, ICML\u201917, pp 1935-1944. JMLR.org"},{"key":"970_CR19","unstructured":"Lakshminarayanan B (2014) Python implementation of the mondrian forest"},{"key":"970_CR20","unstructured":"Lakshminarayanan B, Roy DM, Teh Y\u00a0W (2014) Mondrian Forests: efficient online random forests. In Z.\u00a0Ghahramani, M.\u00a0Welling, C.\u00a0Cortes, N.\u00a0D. Lawrence, and K.\u00a0Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, vol\u00a04, pp 3140\u20133148. Curran Associates, Inc"},{"issue":"23","key":"970_CR21","doi-asserted-by":"publisher","first-page":"7853","DOI":"10.3390\/s21237853","volume":"21","author":"A Logacjov","year":"2021","unstructured":"Logacjov A, Bach K, Kongsvold A, B\u00e5rdstu HB, Mork PJ (2021) HARTH: a human activity recognition dataset for machine learning. Sensors 21(23):7853","journal-title":"Sensors"},{"issue":"22","key":"970_CR22","doi-asserted-by":"publisher","first-page":"6486","DOI":"10.3390\/s20226486","volume":"20","author":"K Martin","year":"2020","unstructured":"Martin K, Tristan Glatard (2020) A benchmark of data stream classification for human activity recognition on connected objects. Sensors (Basel, Switzerland) 20(22):6486","journal-title":"Sensors (Basel, Switzerland)"},{"key":"970_CR23","doi-asserted-by":"crossref","unstructured":"Montiel Jacob, Bifet Albert, Losing Viktor, Read Jesse, Abdessalem Talel (dec 2018) Learning fast and slow: a unified batch\/stream Framework. In: 2018 IEEE international conference on big data (Big Data). IEEE","DOI":"10.1109\/BigData.2018.8622222"},{"key":"970_CR24","doi-asserted-by":"crossref","unstructured":"Morris D, Saponas TS, Guillory A, Kelner I (2014) RecoFit: Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI \u201914, pp 3225-3234, New York, NY, USA. Association for Computing Machinery","DOI":"10.1145\/2556288.2557116"},{"key":"970_CR25","doi-asserted-by":"crossref","unstructured":"Murshed M G Sarwar, Murphy C, Hou D, Khan N, Ananthanarayanan G, Hussain F (2021) Machine learning at the network edge: A survey. ACM Comput. Surv., 54(8)","DOI":"10.1145\/3469029"},{"issue":"4","key":"970_CR26","doi-asserted-by":"publisher","first-page":"6474","DOI":"10.3390\/s140406474","volume":"14","author":"B Oresti","year":"2014","unstructured":"Oresti B, Juan-Manuel G, Miguel D, Hector P, Ignacio R (2014) Window size impact in human activity recognition. Sensors 14(4):6474\u20136499","journal-title":"Sensors"},{"issue":"4","key":"970_CR27","first-page":"1595","volume":"34","author":"PP Ray","year":"2021","unstructured":"Ray PP (2021) A review on tinyml: State-of-the-art and prospects. J King Saud Univ - Comput and Inf Sci 34(4):1595\u20131623","journal-title":"J King Saud Univ - Comput and Inf Sci"},{"key":"970_CR28","doi-asserted-by":"crossref","unstructured":"Reiss Attila, Stricker Didier (2012) Introducing a New Benchmarked Dataset for Activity Monitoring. In 2012 16th international symposium on wearable computers, pp 108\u2013109","DOI":"10.1109\/ISWC.2012.13"},{"key":"970_CR29","doi-asserted-by":"crossref","unstructured":"Sugawara Yu, Oyama Satoshi, Kurihara Masahito (2021) Adaptive rotation forests: Decision tree ensembles for sequential learning. In: 2021 IEEE international conference on systems, Man, and Cybernetics (SMC), pp 613\u2013618","DOI":"10.1109\/SMC52423.2021.9659107"},{"issue":"5","key":"970_CR30","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.3390\/s23052368","volume":"23","author":"A Ustad","year":"2023","unstructured":"Ustad A, Logacjov A, Trolleb\u00f8 S\u00d8, Thingstad P, Vereijken B, Bach K, Maroni NS (2023) Validation of an activity type recognition model classifying daily physical behavior in older adults: the HAR70+ model. Sensors 23(5):2368","journal-title":"Sensors"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-023-00970-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-023-00970-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-023-00970-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T03:08:36Z","timestamp":1708484916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-023-00970-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,17]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["970"],"URL":"https:\/\/doi.org\/10.1007\/s10618-023-00970-4","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2023,10,17]]},"assertion":[{"value":"20 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","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 computing platform was obtained with funding from the Canada Foundation for Innovation. The authors have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}