Different PCA scenarios for email filtering
Issam J. Dagher, Rima Antoun · International Journal of Computers and Applications · 2016
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:• 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 of PCADR with Support Vector Machine and Bayes detector was done to prove its superior behavior.