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dc.contributor.authorCarmona-Cejudo, José M.
dc.contributor.authorBaena-García, Manuel
dc.contributor.authorCampo-Ávila, José
dc.contributor.authorBifet, Albert
dc.contributor.authorGama, João
dc.contributor.authorMorales-Bueno, Rafael
dc.coverage.spatialConference held at Porto, Portugalen_NZ
dc.identifier.citationCarmona-Cejudo, J. M., Baena-García, M., Campo-Ávila, J., Bifet, A., Gama, J., & Morales-Bueno, R. (2011). Lecture Notes in Computer Science. (D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, & J. Hollmén, Eds.) (Vol. 7014, pp. 90-100).en_NZ
dc.description.abstractReal-time email classification is a challenging task because of its online nature, subject to concept-drift. Identifying spam, where only two labels exist, has received great attention in the literature. We are nevertheless interested in classification involving multiple folders, which is an additional source of complexity. Moreover, neither cross-validation nor other sampling procedures are suitable for data streams evaluation. Therefore, other metrics, like the prequential error, have been proposed. However, the prequential error poses some problems, which can be alleviated by using mechanisms such as fading factors. In this paper we present GNUsmail, an open-source extensible framework for email classification, and focus on its ability to perform online evaluation. GNUsmail’s architecture supports incremental and online learning, and it can be used to compare different online mining methods, using state-of-art evaluation metrics. We show how GNUsmail can be used to compare different algorithms, including a tool for launching replicable experiments.en_NZ
dc.sourceIDA 2011en_NZ
dc.subjectEmail Classificationen_NZ
dc.subjectOnline Methodsen_NZ
dc.subjectConcept Driften_NZ
dc.subjectText Miningen_NZ
dc.subjectMachine learning
dc.titleOnline evaluation of email streaming classifiers using GNUsmailen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProc 10th International Symposium on Advances in Intelligent Data Analysis Xen_NZ
pubs.volumeLNCS 7014en_NZ

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