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dc.contributor.authoramelec, viloria
dc.contributor.authorPineda Lezama, Omar Bonerge
dc.contributor.authorChang, Eduardo
dc.description.abstractOne problem in classifying tasks is the handling of features that characterize classes. When the list of features is long, a noise resistant algorithm of irrelevant features can be used, or these features can be reduced. Authorship attribution is a task that assigns an anonymous text to a subject on a list of possible authors, has been widely addressed as an automatic text classification task. In it, n-grams can produce long lists of features even in small corpora. Despite this, there is a lack of research exposing the effects of using noise-resistant algorithms, reducing traits, or combining both options. This paper responds to this lack by using contributions to discussion forums related to organized crime. The results show that the classifiers evaluated, in general, benefit from feature reduction, and that, thanks to such reduction, even classical algorithms outperform state-of-the-art classifiers considered highly noise
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceProcedia Computer Sciencespa
dc.subjectAuthorship attributionspa
dc.subjectClassification featuresspa
dc.subjectNoise resistant algorithmsspa
dc.subjectFeature reductionspa
dc.titleClassification of authors for an automatic recommendation process for criminal responsibilityspa
dc.typeArtículo de revistaspa
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CC0 1.0 Universal
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