Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5341
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dc.contributor.authorFelipe Azevedo Pinagéen
dc.contributor.authordos Santos,EMen
dc.contributor.authorJoão Gamaen
dc.date.accessioned2018-01-03T10:37:40Z-
dc.date.available2018-01-03T10:37:40Z-
dc.date.issued2016en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/5341-
dc.identifier.urihttp://dx.doi.org/10.1002/widm.1184en
dc.description.abstractData mining and machine learning algorithms can be employed to perform a variety of tasks. However, since most of these problems may depend on environments that change over time, performing classification tasks in dynamic environments has been a challenge in data mining research domain in the last decades. Currently, in the literature, the most common strategies used to detect changes are based on accuracy monitoring, which relies on previous knowledge of the data in order to identify whether or not correct classifications are provided. However, such a feedback can be infeasible in practical problems. In this work, we present a comprehensive overview of current machine learning/data mining approaches proposed to deal with dynamic environments problems. The objective is to highlight the main drawbacks and open issues, as well as future directions and problems worthy of investigation. In addition, we provide the definitions of the main terms used to represent this problem in the literature, such as concept drift and novelty detection. WIREs Data Mining Knowl Discov 2016, 6:156-166. doi: 10.1002/widm.1184 For further resources related to this article, please visit the .en
dc.languageengen
dc.relation6560en
dc.relation5120en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleClassification systems in dynamic environments: an overviewen
dc.typearticleen
dc.typePublicationen
Appears in Collections:LIAAD - Articles in International Journals

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