SUPPORT VECTOR MACHINE FOR PERSONALIZED E-MAIL SPAM FILTERING

E-mail is one of the most frequently used personal and official communication tool over the Internet. The continually increasing ratio of spam e-mails over legitimate emails and adversarial nature of spam e-mails lead to the requirement of employing spam filter that can be updated dynamically. Moreover, the discrimination criteria of spam and legitimate e-mails vary for different users worldwide. This leads to the personalization of e-mail spam filter which automatically adapts individual user's characteristics. We propose an incremental learning model for personalized e-mail spam filtering. We apply support vector machine - a supervised machine learning algorithm & a discriminative classifier for the designing the classification model. We apply incremental learning using support vector machine for the development of a dynamically updated filter. Our model is evaluated on two different datasets that consist of a set of e-mails structured according to the order of arrival. Experimental results confirm the superior performance of incremental learning over the batch learning model. The inclusion of incremental learning when the distribution of data is different in training and testing sets helps improving classification accuracy and decreases the false positive rate substantially.