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Data"],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>\n            Learning from streaming data is challenging as the distribution of incoming data may change over time, a phenomenon known as concept drift. The predictive patterns, or\n            <jats:italic>experience<\/jats:italic>\n            learned under one distribution may become irrelevant as conditions change under concept drift, but may become relevant once again when conditions reoccur. Adaptive learning methods adapt a classifier to concept drift by identifying which distribution, or\n            <jats:italic>concept<\/jats:italic>\n            , is currently present in order to determine which experience is relevant. Identifying a concept requires some\n            <jats:italic>representation<\/jats:italic>\n            to be stored for comparison, with the quality of the representation being key to accurate identification. Existing concept representations are based on meta-features, efficient univariate summaries of a concept. However, no single meta-feature can fully represent a concept, leading to severe accuracy loss when existing representations cannot describe concept drift. To avoid these failure cases, we propose the first general framework for combining a diverse range of meta-features into a single representation. We solve two main challenges, first presenting a method of efficiently computing, storing, and querying an arbitrary set of meta-features as a single representation, showing that a combination of meta-features may successfully avoid failure cases seen with existing methods. Second, we present the first method for dynamically learning which meta-features distinguish concepts in any given dataset, significantly improving performance. Our proposed approach enables state-of-the-art feature selection methods, such as mutual information, to be applied to concept representation meta-features for the first time. We investigate tradeoffs between memory budget and classification performance, observing accuracy increases of up to 16% by dynamically weighting the contribution of each meta-feature.\n          <\/jats:p>","DOI":"10.1145\/3587098","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T09:20:24Z","timestamp":1678180824000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1597-4284","authenticated-orcid":false,"given":"Ben","family":"Halstead","sequence":"first","affiliation":[{"name":"University of Auckland, Auckland, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7256-4049","authenticated-orcid":false,"given":"Yun Sing","family":"Koh","sequence":"additional","affiliation":[{"name":"University of Auckland, Auckland, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-0053","authenticated-orcid":false,"given":"Patricia","family":"Riddle","sequence":"additional","affiliation":[{"name":"University of Auckland, Auckland, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4955-0743","authenticated-orcid":false,"given":"Mykola","family":"Pechenizkiy","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, AE Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8339-7773","authenticated-orcid":false,"given":"Albert","family":"Bifet","sequence":"additional","affiliation":[{"name":"University of Waikato and LTCI, T\u00e9l\u00e9com Paris, IP-Paris"}]}],"member":"320","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1070-0"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2507123"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2239309"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50127-7_17"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112832"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.09.031"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.08.023"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"e_1_3_1_10_2","first-page":"87","volume-title":"Proceedings of the International Workshop on New Frontiers in Mining Complex Patterns","author":"Brzezinski Dariusz","year":"2014","unstructured":"Dariusz Brzezinski and Jerzy Stefanowski. 2014. 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