VARIOUS SPAMS AND CLASSIFICATION ALGORITHMS FOR DETECTION OF SPAM EMAIL THROUGH COMPARING J48, SVM AND NAIVE BAYES CLASSIFIERS USING WEKA TOOL

Vutharkar Nagaveni, Dr. Vimal Pandya

Abstract


Electronic Mail (E-mail) is playing the most important and significant role taken in the world of information communication for users. Nowadays, Email is the most common and effective mode of communication technology for communication and sharing information with both end-users. With the rapid increase of email users, there will be an increase in the volume of spam emails too from the past few decades. Emails are categorized into ham and spam emails. This paper illustrates on different existing email spam filter system regarding Machine Learning Technique (MLT) such as Naive Bayes, SVM, J48 and presents the classifications, evaluation, and comparison on different email spam filtering algorithms using WEKA Software and performs various parameters like finding Accuracy, Recall, Precision, Measures and False Position Rate, etc. The final output result should be ‘1’ if it is finally spam present, otherwise, it should be ‘0’ for non-spam. In this analysis, the Final out presents that the J48 classifier is the best and efficient algorithm for spam or not spam emails among other algorithms.

Keywords


WEKA, Support vector Machine, Email, J48, Naive Bayes, Spam.

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References


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