| dc.contributor.author | Labuschagne, Pieter | |
| dc.contributor.author | Du Toit, Jan V. | |
| dc.date.accessioned | 2016-02-11T08:59:17Z | |
| dc.date.available | 2016-02-11T08:59:17Z | |
| dc.date.issued | 2014 | |
| dc.identifier.citation | Labuschagne, 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.isbn | 978-0-620-61965-3 | |
| dc.identifier.uri | http://hdl.handle.net/10394/16258 | |
| dc.identifier.uri | http://www.satnac.org.za/proceedings/2014/SATNAC%202014%20Conference%20Proceedings_USB_edition.pdf | |
| dc.description.abstract | Solutions 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 choice | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | SATNAC | en_US |
| dc.subject | Classification | en_US |
| dc.subject | ensemble | en_US |
| dc.subject | GANN | en_US |
| dc.subject | spam | en_US |
| dc.title | Spam email classifcation with generalized additive neural networks using ensemble methods | en_US |
| dc.type | Presentation | en_US |
| dc.contributor.researchID | 21269777 - Labuschagne, Pieter | |
| dc.contributor.researchID | 10789901 - Du Toit, Jan Valentine | |
