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dc.contributor.authorDagher, Issamen_US
dc.contributor.authorAntoun, Rimaen_US
dc.description.abstractThe objective of this paper is to discuss different scenarios for Principal Component Analysis classifier implemented for email filtering process (Ham vs. spam emails). The study highlights on the variation of the accuracy of these classifiers with respect to the variation in feature preprocessing. Four scenarios were considered: Scenario 1: Ham and Spam classes are represented with different features. Scenario 2: Ham and Spam classes are represented with same features. Scenario 3: Ham and Spam classes are represented with common terms. Scenario 4: Ham and Spam classes are represented with common Features and Characteristic terms. Different experiments were done using a public corpus extracted from the University of California-Irvine Machine Learning Repository. Different training and test sets were used. A comparison with Support Vector Machine and Bayes detector was done to prove its superior behavior.en_US
dc.format.extent4 p.en_US
dc.subjectFeature extractionen_US
dc.subjectInformation filteringen_US
dc.subjectPattern classificationen_US
dc.subjectText analysisen_US
dc.subjectUnsolicited e-mailen_US
dc.subject.lcshPrincipal component analysisen_US
dc.titleHam-Spam Filtering Using Different PCA Scenariosen_US
dc.typeConference Paperen_US
dc.relation.conferenceIEEE International Conference on Computational Science and Engineering CSE2016 (19th : 24-26 Aug. 2016 : Paris, France)en_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.relation.ispartoftext2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES)en_US
Appears in Collections:Department of Computer Engineering
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