Data Stream Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency

dc.contributor.author Souza,VMAd en
dc.contributor.author Silva,DF en
dc.contributor.author João Gama en
dc.contributor.author Batista,GEAPA en
dc.date.accessioned 2018-01-03T10:36:14Z
dc.date.available 2018-01-03T10:36:14Z
dc.date.issued 2015 en
dc.description.abstract Data stream classification algorithms for nonstationary environments frequently assume the availability of class labels, instantly or with some lag after the classification. However, certain applications, mainly those related to sensors and robotics, involve high costs to obtain new labels during the classification phase. Such a scenario in which the actual labels of processed data are never available is called extreme verification latency. Extreme verification latency requires new classification methods capable of adapting to possible changes over time without external supervision. This paper presents a fast, simple, intuitive and accurate algorithm to classify nonstationary data streams in an extreme verification latency scenario, namely Stream Classification Algorithm Guided by Clustering - SCARGC. Our method consists of a clustering followed by a classification step applied repeatedly in a closed loop fashion. We show in several classification tasks evaluated in synthetic and real data that our method is faster and more accurate than the state-of-the-art. Copyright © SIAM. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5325
dc.identifier.uri http://dx.doi.org/10.1137/1.9781611974010.98 en
dc.language eng en
dc.relation 5120 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Data Stream Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency en
dc.type conferenceObject en
dc.type Publication en
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