dc.creatorOliveira, Luan Soares
dc.creatorBatista, Gustavo Enrique de Almeida Prado Alves
dc.date.accessioned2016-03-17T19:25:16Z
dc.date.accessioned2018-07-04T17:09:45Z
dc.date.available2016-03-17T19:25:16Z
dc.date.available2018-07-04T17:09:45Z
dc.date.created2016-03-17T19:25:16Z
dc.date.issued2015-11
dc.identifierBrazilian Conference on Intelligent Systems, IV, 2015, Natal.
dc.identifier9781509000166
dc.identifierhttp://www.producao.usp.br/handle/BDPI/49973
dc.identifierhttp://dx.doi.org/10.1109/BRACIS.2015.61
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1645494
dc.description.abstractLearning concepts from data streams differs significantly from traditional batch learning, because in data streams the concepts to be learned may evolve over time. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, outdated concepts can cause misclassifications. Although several incremental Gaussian mixture models methods have been proposed in the literature, we notice that these algorithms lack an explicit policy to discard outdated concepts. In this paper, we propose a new incremental algorithm for data stream learning based on Gaussian Mixture Models. The proposed method is compared to various algorithms widely used in the literature, and the results show that it is competitive with them in various scenarios, overcoming them in some cases.
dc.languageeng
dc.publisherUniversidade Federal do Rio Grande do Norte – UFRN
dc.publisherSociedade Brasileira de Computação – SBC
dc.publisherNatal
dc.relationBrazilian Conference on Intelligent Systems, IV
dc.rightsCopyright IEEE
dc.rightsclosedAccess
dc.subjectIncremental learning
dc.subjectdata stream
dc.subjectconcept drift
dc.subjectgaussian mixture model
dc.titleIGMM-CD: a gaussian mixture classification algorithm for data streams with concept drifts
dc.typeActas de congresos


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