Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/595
Title: Ham-Spam Filtering Using Different PCA Scenarios
Authors: Dagher, Issam 
Antoun, Rima
Affiliations: Department of Computer Engineering 
Keywords: Feature extraction
Information filtering
Pattern classification
Text analysis
Unsolicited e-mail
Subjects: Principal component analysis
Issue Date: 2017
Publisher: IEEE
Part of: 2016 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)
Start page: 542
End page: 545
Conference: IEEE International Conference on Computational Science and Engineering CSE2016 (19th : 24-26 Aug. 2016 : Paris, France) 
Abstract: 
The 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.
URI: https://scholarhub.balamand.edu.lb/handle/uob/595
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Computer Engineering

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