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SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared

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dc.title SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared en
dc.contributor.author Meli, Clyde
dc.contributor.author Komínková Oplatková, Zuzana
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-33620-6
dc.identifier.isbn 978-3-319-33622-0
dc.date.issued 2016
utb.relation.volume 465
dc.citation.spage 399
dc.citation.epage 411
dc.event.title 5th Computer Science On-line Conference, CSOC 2016
dc.event.location Prague
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2015-04-27
dc.event.edate 2015-04-30
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-33622-0_36
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-33622-0_36
dc.subject Genetic programming (GP) en
dc.subject Linear genetic programming (LGP) en
dc.subject Naïve bayesian classifier en
dc.subject Reverse polish notation (RPN) en
dc.subject Spam detection en
dc.description.abstract An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters. © Springer International Publishing Switzerland 2016. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006428
utb.identifier.obdid 43876369
utb.identifier.scopus 2-s2.0-84964721013
utb.identifier.wok 000385788200036
utb.source d-scopus
dc.date.accessioned 2016-07-26T14:58:30Z
dc.date.available 2016-07-26T14:58:30Z
dc.rights.access openAccess
utb.identifier.utb-sysno 87686
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Clyde Meli and Zuzana Kominkova Oplatkova C. Meli ( ✉ ) CIS Department, Faculty of ICT, University of Malta, Msida, Malta e-mail: Clyde.meli@um.edu.mt Z.K. Oplatkova Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam. T.G. Masaryka 5555, Zlín, Czech Republic e-mail: oplatkova@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship Acknowledgements go to my Ph.D. Supervisors Dr Vitezlav Nezval. Thanks also to Tom Fawcett who answered my email query about the subject of Bayesian classifiers and RPN. This work was supported by Grant Agency of the Czech Republic—GACR P103/15/06700S, further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089.
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