COMPARISON OF MACHINE LEARNING TECHNIQUES IN SPAM E-MAIL CLASSIFICATION

Abstract

E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails is an important problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares different Machine Learning Techniques in classification of spam e-mails. Random Forest (RF), C4.5 decision tree and Artificial Neural Network (ANN) were tested to determine which method provides the best results in spam e-mail classification. Our results show that RF is the best technique applied on dataset from HP Labs, indicating that ensemble methods may have an edge in spam detection