SF-HME system: A hierarchical mixtures-of-experts classification system for spam filtering

Belsis, P; Fragos, K; Gritzalis, S; Skourlas, C

dc.contributor.author	Belsis, P	en
dc.contributor.author	Fragos, K	en
dc.contributor.author	Gritzalis, S	en
dc.contributor.author	Skourlas, C	en
dc.date.accessioned	2014-03-01T02:44:11Z
dc.date.available	2014-03-01T02:44:11Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31740
dc.subject	Hierarchical systems of experts	en
dc.subject	Machine learning	en
dc.subject	Spam mail	en
dc.subject.other	Adaptive filtering	en
dc.subject.other	Algorithms	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Electronic mail	en
dc.subject.other	Learning systems	en
dc.subject.other	Text processing	en
dc.subject.other	Hierarchical Mixture of Experts system	en
dc.subject.other	Hierarchical systems of experts	en
dc.subject.other	Nonlinear relationships	en
dc.subject.other	Spam mail	en
dc.subject.other	Hierarchical systems	en
dc.title	SF-HME system: A hierarchical mixtures-of-experts classification system for spam filtering	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1141277.1141360	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1141277.1141360	en
heal.publicationDate	2006	en
heal.abstract	Many linear statistical models have been lately proposed in text classification related literature and evaluated against the Unsolicited Bulk Email filtering problem. Despite their popularity - due both to their simplicity and relative ease of interpretation -the non-linearity assumption of data samples is inappropriate in practice, due to its inability to capture the apparent non-linear relationships, which characterize these samples. In this paper, we propose the SF-HME, a Hierarchical Mixture-of-Experts system, attempting to overcome limitations common to other machinelearning based approaches when applied to spam mail classification. By reducing the dimensionality of data through the usage of the effective Simba algorithm for feature selection, we evaluated our SF-HME system with a publicly available corpus of emails, with very high similarity between legitimate and bulk email - and thus low discriminative potential - where the traditional rule based filtering approaches achieve considerable lower degrees of precision. As a result, we confirm the domination of our SF-HME method against other machine learning approaches, which appeared to present lesser degree of recall. Copyright 2006 ACM.	en
heal.journalName	Proceedings of the ACM Symposium on Applied Computing	en
dc.identifier.doi	10.1145/1141277.1141360	en
dc.identifier.volume	1	en
dc.identifier.spage	354	en
dc.identifier.epage	360	en