Abstract
Spam in emails has become a major issue. Spam messages consume space, network bandwidth and are of no use to the receiver. It is very difficult to filter spam as spammers try to tackle the processes carried out by the filtering mechanism. Various classification algorithms are used to classify a mail as spam or non-spam (ham). The present paper compares and discusses the effectiveness of four machine learning classification algorithms, belonging to different categories (Probabilistic, Decision Tree, Vector Machines and Lazy Algorithms) on the basis of various performance measures, using WEKA, a data mining tool to analyze different algorithms. Enron dataset is taken in a processed form from Athens University of Economics and Business and it is found that J48 and BayesNet algorithms perform better than SVM.
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Acknowledgments
Aakanksha Sharaff, Naresh Kumar Nagwani would like to thanks National Institute of Technology, Raipur, India for providing their support, imparting their knowledge, and making all the required facilities available for completing this research.
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Sharaff, A., Nagwani, N.K., Dhadse, A. (2016). Comparative Study of Classification Algorithms for Spam Email Detection. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2553-9_23
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DOI: https://doi.org/10.1007/978-81-322-2553-9_23
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