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dc.contributor.authorBasto-Fernandes, V.en
dc.contributor.authorYevseyeva, Irynaen
dc.contributor.authorMendez, J. R.en
dc.contributor.authorZhao, J.en
dc.contributor.authorFdez-Riverola, F.en
dc.contributor.authorEmmerich, M. T. M.en
dc.date.accessioned2016-08-31T13:53:15Z
dc.date.available2016-08-31T13:53:15Z
dc.date.issued2016-07-04
dc.identifier.citationBasto-Fernandes V., Yevseyeva I., Mendez J.R., Zhao J., Fdez-Riverola F. Emmerich M.T.M. (2016) A spam filtering mult-objective optimization study covering parsimony maximization and three-way classification. Applied Soft Computing. 48, pp. 111-123en
dc.identifier.urihttp://hdl.handle.net/2086/12477
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractClassifier performance optimization in machine learning can be stated as a multi-objective optimization problem. In this context, recent works have shown the utility of simple evolutionary multi-objective algorithms (NSGA-II, SPEA2) to conveniently optimize the global performance of different anti-spam filters. The present work extends existing contributions in the spam filtering domain by using three novel indicator-based (SMS-EMOA, CH-EMOA) and decomposition-based (MOEA/D) evolutionary multi-objective algorithms. The proposed approaches are used to optimize the performance of a heterogeneous ensemble of classifiers into two different but complementary scenarios: parsimony maximization and e-mail classification under low confidence level. Experimental results using a publicly available standard corpus allowed us to identify interesting conclusions regarding both the utility of rule-based classification filters and the appropriateness of a three-way classification system in the spam filtering domain.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectSpam filteringen
dc.subjectMulti-objective optimizationen
dc.subjectParsimonyen
dc.subjectThree-way classificationen
dc.subjectRule-based classifiersen
dc.subjectSpamAssassinen
dc.titleA spam filtering mult-iobjective optimization study covering parsimony maximization and three-way classificationen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.asoc.2016.06.043
dc.researchgroupCyber Security Centreen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2016-06-27en
dc.researchinstituteCyber Technology Institute (CTI)en


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