{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:42:27Z","timestamp":1775562147288,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil (CAPES-PROEX)","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil (CAPES-PROEX)","award":["2024\/2025"],"award-info":[{"award-number":["2024\/2025"]}]},{"name":"Amazonas State Research Support Foundation","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Amazonas State Research Support Foundation","award":["2024\/2025"],"award-info":[{"award-number":["2024\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Concept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies. However, these strategies often rely on complete classifier retraining without considering the properties of the drift, which can prove inadequate in many scenarios. Limited attention has been given to understanding the nature of drift and its characterization, which are crucial for designing effective reaction strategies. Drift descriptors provide a means to explain how new concepts replace existing ones, offering valuable insights into the nature of drift. In this context, this work examines the impact of four descriptors\u2014severity, recurrence, frequency, and speed\u2014on concept drift through extensive theoretical and experimental analysis. Experiments were conducted on five datasets with 32 descriptor variations, eight drift detectors, and a non-detection context, resulting in 1440 combinations. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency descriptors have the highest impact, whereas speed has minimal influence; and (iii) there is a need to incorporate mechanisms for describing concept drift into the strategies designed to address it.<\/jats:p>","DOI":"10.3390\/informatics12010013","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T09:16:26Z","timestamp":1738314986000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Analysis of Descriptors of Concept Drift and Their Impacts"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7445-2331","authenticated-orcid":false,"given":"Albert","family":"Costa","sequence":"first","affiliation":[{"name":"Institute of Computing, Federal University of Amazonas, Av. Rodrigo Ot\u00e1vio, n\u00ba 6200, Coroado I, Campus Universit\u00e1rio Senador Arthur Virg\u00edlio Filho, Setor Norte, Manaus 69080-900, AM, Brazil"}]},{"given":"Rafael","family":"Giusti","sequence":"additional","affiliation":[{"name":"Institute of Computing, Federal University of Amazonas, Av. Rodrigo Ot\u00e1vio, n\u00ba 6200, Coroado I, Campus Universit\u00e1rio Senador Arthur Virg\u00edlio Filho, Setor Norte, Manaus 69080-900, AM, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5671-581X","authenticated-orcid":false,"given":"Eulanda M.","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Institute of Computing, Federal University of Amazonas, Av. Rodrigo Ot\u00e1vio, n\u00ba 6200, Coroado I, Campus Universit\u00e1rio Senador Arthur Virg\u00edlio Filho, Setor Norte, Manaus 69080-900, AM, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1381","DOI":"10.1002\/widm.1381","article-title":"An overview of unsupervised drift detection methods","volume":"10","author":"Gemaque","year":"2020","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Farka\u0161, I., Masulli, P., and Wermter, S. (2020, January 15\u201318). Explaining Concept Drift by Mean of Direction. 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