An Approach to Detect Spam Emails by Using Majority Voting
Roohi Hussain, Usman Qamar · International Conference on Data Mining · 2014
Internet usage has become intensive during the last few decades; this has given rise to the use of email which is one of the fastest yet cheap modes of communication. The growing demand of email communication has given rise to the spam email which is also known as unsolicited mails. In this paper we propose an ensemble model that uses majority voting on top of several classifiers to detect spam. The classification algorithms used for this purpose are Naive Bayesian, Support Vector Machines, Random Forest, Decision Stump and kNearest Neighbor. Majority voting generates the final decision of the ensemble by obtaining major votes from the classifiers. The sample dataset used for this task is taken from UCI and the tool Rapidminer is used for the validation of the results.