Email spam classification using neighbor probability based Naïve Bayes algorithm

Abstract

Email spam is a kind of electronic spam, which tends to be a more difficult problem nowadays among all internet challenges. Spam mails are mostly sent in commercial purpose, some of them may contain malware links that lead to phishing websites. The aim of this study is to classify into ham and spam emails with an optimized and well efficient classification technique. Ham holds emails that are legitimate or legally valid message can get accepted by users. Spam emails are unwanted emails that a user doesn't want and to get rid of it. This study emphasizes on the improvement in classifying all mails into these two groups with minimal requirement of training and with an accuracy of hundred percent. Here in this study, Modified Naïve Bayes (MNB) classifier ensured the requirements with very low percentage of training and produces accurate results than existing Naïve Bayes (NB) or Supporting Vector Machine (SVM) classifier.