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dc.contributor.authorGemaque, Rosana Noronha-
dc.contributor.authorCosta, Albert França Josuá-
dc.contributor.authorGiusti, Rafael-
dc.contributor.authorDos Santos, Eulanda Miranda-
dc.date.accessioned2020-08-14T20:50:47Z-
dc.date.available2020-08-14T20:50:47Z-
dc.date.issued2020-
dc.identifier.urihttps://repositorio.inpa.gov.br/handle/1/36333-
dc.language.isoenpt_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/br/*
dc.subjectData streamspt_BR
dc.subjectEngineering educationpt_BR
dc.subjectMachine learningpt_BR
dc.subjectClassification technologypt_BR
dc.subjectCustomer preferencespt_BR
dc.subjectEnsemble of classifierspt_BR
dc.subjectMachine learning modelspt_BR
dc.subjectPractical problemspt_BR
dc.subjectReal-time analyticspt_BR
dc.subjectState-of-the-art approachpt_BR
dc.subjectUnsupervised methodpt_BR
dc.subjectData miningpt_BR
dc.titleAn overview of unsupervised drift detection methodspt_BR
dc.typeArtigopt_BR
dc.identifier.doi10.1002/widm.1381-
dc.publisher.journalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discoverypt_BR
dc.description.resumoPractical applications involving big data, such as weather monitoring, identification of customer preferences, Internet log analysis, and sensors warnings require challenging data analysis, since these are examples of problems whose data are generated in streams and usually demand real-time analytics. Patterns in such data stream problems may change quickly. Consequently, machine learning models that operate in this context must be updated over time. This phenomenon is called concept drift in machine learning and data mining literature. Several different directions have been pursued to learn from data stream and to deal with concept drift. However, most drift detection methods consider that an instance's class label is available right after its prediction, since these methods work by monitoring the prediction results of a base classifier or an ensemble of classifiers. Nevertheless, this constraint is unrealistic in several practical problems. To cope with this constraint, some works are focused on proposing efficient unsupervised or semi-supervised concept drift detectors. While interesting and recent overview papers dedicated to supervised drift detectors have been published, the scenario is not the same in terms of unsupervised methods. Therefore, this work presents a comprehensive overview of approaches that tackle concept drift in classification problems in an unsupervised manner. Additional contribution includes a proposed taxonomy of state-of-the-art approaches for concept drift detection based on unsupervised strategies. This article is categorized under: Technologies > Classification Technologies > Machine Learning. © 2020 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals LLC.pt_BR
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