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.

Naive Bayesian Spam Filtering

“…These staggering numbers show how potentially dangerous it is to use this seemingly simple but effective communication tool in Figure 2 So far, there are many spam filtering techniques. Decision tree is one of them, [7] a method of supervised learning in which the main idea is top-down divide and conquer. First recurse upwards from the root position, find an attribute that can be divided at the intermediate node, all subsets continue to be recursively divided according to its internal nodes, if these subsets can be correctly classified, then the leaf nodes can be constructed, these subsets also need Classification to the corresponding leaf node, when each subset is classified to the leaf node, the decision tree is formed.…”