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Email is one of the most economical and fast communication means in recent years; however, there has been a high increase in the rate of spam emails in recent times due to the increased number of email users. Emails are mainly classified into spam and non-spam categories using data mining classification techniques. This paper provides a description and comparative for the evaluation of effective classifiers using three algorithms namely k-nearest neighbor, Naive Bayesian, and support vector machine. Seven spam email datasets were used to conducted experiment in the MATLAB environment without using any feature selection method. The simulation results showed SVM classifier to achieve a better classification accuracy compared to the K-NN and NB. Key-Words: Classification Accuracy; KNN; SVM; NB; Email Spam

Evaluation of the Performance for Popular Three Classifiers on Spam Email without using FS methods