A survey and evaluation of supervised machine learning techniques for spam e-mail filtering

Emails are used in most of the fields of education and business. They can be classified into ham and spam and with their increasing use, the ratio of spam is increasing day by day. There are several machine learning techniques, which provides spam mail filtering methods, such as Clustering, J48, Naïve Bayes etc. This paper considers different classification techniques using WEKA to filter spam mails. Result shows that Naïve Bayes technique provides good accuracy (near to highest) and take least time among other techniques. Also a comparative study of each technique in terms of accuracy and time taken is provided.