Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison Study

Abstract

Emails have become the most economical and fastest communication forms. However, during the past few years, the increment of email users has dramatically increased spam emails. Various anti-spam techniques have been developed to minimize if not eliminate the spam problem. In this paper, we study the disparity in the effectiveness of using different decision tree algorithms in email classification and combat spam problems. For that, we have chosen Universiti Utara Malaysia emails as a case study. To achieve the best possible classification accuracy, we compared all chosen algorithms’ performance, which are Random Forest, LMT, Decision Stump, J48, Random Tree, and REP Tree. The experimental results showed that the Decision Stump algorithm is more effective to be used in classifying the emails, and the F-measures, Precision, and recall score for the Decision Stump algorithm are higher than the other comparison algorithms.