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dc.contributor.authorMéndez, Jose R.
dc.contributor.authorFernández Riverola, Florentino
dc.contributor.authorDíaz Gómez, Fernando
dc.contributor.authorIglesias, E. L.
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2017-09-06T09:16:07Z
dc.date.available2017-09-06T09:16:07Z
dc.date.issued2006-07
dc.identifier.citationAdvances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4065, pp. 106-120.
dc.identifier.isbn978-3-540-36036-0 (Print) / 978-3-540-36037-7 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135054
dc.description.abstractIn this paper we analyse the strengths and weaknesses of the mainly used feature selection methods in text categorization when they are applied to the spam problem domain. Several experiments with different feature selection methods and content-based filtering techniques are carried out and discussed. Information Gain, χ 2-text, Mutual Information and Document Frequency feature selection methods have been analysed in conjunction with Naïve Bayes, boosting trees, Support Vector Machines and ECUE models in different scenarios. From the experiments carried out the underlying ideas behind feature selection methods are identified and applied for improving the feature selection process of SpamHunting, a novel anti-spam filtering software able to accurate classify suspicious e-mails.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleA Comparative Performance Study of Feature Selection Methods for the Anti-spam Filtering Domain
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
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