SPAM FILTERING – A COMPARATIVE STUDY OF THE PERFORMANCE OF DIFFERENT CLASSIFIERS FOR EFFECTIVE FILTERING

Dhananjay Tyagi

Abstract: Electronic mail is used daily by billions of people to interact and communicate around the world and is a critical application for many businesses. Over the last couple of decades unsolicited bulk email has become a headache for the email user. A staggering amount of spam is streaming into user’s mailboxes daily. Spam is not only irritating for most email users but it also overtaxes the IT infrastructure of businesses and costs billions of dollars in wasted productivity. The need of effective spam filtering techniques increases. Machine learning algorithms can be used with current spam filtering schemes for increased efficiency. This paper presents a comparative study of the performance of different Machine Learning Algorithms which can be used to filter a mail as spam or ham.

Keywords: Spam, Machine Learning, AdaBoost, Naïve Bayes, K-NN, SVM

DOI: https://doi.org/10.15623/ijret.2016.0509052