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Title: Different PCA scenarios for email filtering
Authors: Dagher, Issam 
Antoun, Rima
Affiliations: Department of Computer Engineering 
Keywords: Ham
Issue Date: 2016
Part of: International journal of computers and applications
Volume: 38
Issue: 1
Start page: 41
End page: 45
Improving email filtering (Ham vs. Spam emails) is a very important process. The objective of this paper is to increase the filtering accuracy and to decrease the processing time. It discusses different scenarios for Principal Component Analysis-Document Reconstruction (PCADR) classifier implemented for email filtering process The study highlights on the variation in the accuracy of a PCADR classifier with respect to the variation in feature preprocessing. Four scenarios were considered:{09} Scenario 1: Ham and Spam classes are represented with different features.{09} Scenario 2: Ham and Spam classes are represented with same features.{09} Scenario 3: Ham and Spam classes are represented with common terms.{09} 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 of PCADR with Support Vector Machine and Bayes detector was done to prove its superior behavior.
DOI: 10.1080/1206212X.2016.1218237
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Computer Engineering

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