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dc.contributor.authorLabuschagne, Pieter
dc.contributor.authorDu Toit, Jan V.
dc.date.accessioned2016-02-11T08:59:17Z
dc.date.available2016-02-11T08:59:17Z
dc.date.issued2014
dc.identifier.citationLabuschagne, P. & Du Toit, J.V. 2014. Spam email classifcation with generalized additive neural networks using ensemble methods. SATNAC 2014 conference. 31 Aug - 3 Sep. [http://www.satnac.org.za/proceedings/2014/SATNAC%202014%20Conference%20Proceedings_USB_edition.pdf]en_US
dc.identifier.isbn978-0-620-61965-3
dc.identifier.urihttp://hdl.handle.net/10394/16258
dc.identifier.urihttp://www.satnac.org.za/proceedings/2014/SATNAC%202014%20Conference%20Proceedings_USB_edition.pdf
dc.description.abstractSolutions to minimize the amount of junk mail we receive are not yet optimal and thus research towards an improved spam email classifier is ongoing. Traditional content and listbased filters are less effective than machine learning approaches, since continuous maintenance and human input are necessary. In this paper an automated construction algorithm for a Generalized Additive Neural Network (GANN) is applied to the Enron and PU1 email corpora and compared to the popular Naive Bayesian filter. In an attempt to further enhance the GANN performance, both Bagging and Boosting ensemble methods were applied. It was found that both ensemble methods improved results making the GANN the better choiceen_US
dc.language.isoenen_US
dc.publisherSATNACen_US
dc.subjectClassificationen_US
dc.subjectensembleen_US
dc.subjectGANNen_US
dc.subjectspamen_US
dc.titleSpam email classifcation with generalized additive neural networks using ensemble methodsen_US
dc.typePresentationen_US
dc.contributor.researchID21269777 - Labuschagne, Pieter
dc.contributor.researchID10789901 - Du Toit, Jan Valentine


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