A COMPARATIVE STUDY ON SPAM EMAIL: DATA ANALYSIS BY VARIOUS CLASSIFICATION ALGORITHMS ALONG WITH JUSTIFICATION OF J48
Keywords:
Classification, Accuracy, SVM, J48, Naive Bayes, WEKAAbstract
Nowadays email becomes one among the fastest and most economical and effective media of communication. Hence as increase of email users dramatically increase of spam emails during the past few years. The data mining classification algorithms are classified into categorize this email as spam or non-spam. During this paper, we conducted experiment within the WEKA environment by using three algorithms namely Naive Bayes, J48, Support Vector Machine (SVM) on the spam email dataset and later the three algorithms were compared in terms of classification accuracy. The in-depth analysis of the study and descriptions of the three classification algorithms is presented consistent with our data simulation results the J48 classifier outstanding performs than Naive Bayes and SVM in terms of classification accuracy level performance.
References
Ghada Hammad AL-Rawashdeh,” Comparison of four email classification algorithms using WEKA”,International Journal of Computer Science and Information Security (IJCSIS),
Vol. 17, No. 2, February 2019.
Aman Kumar Sharma, “A Comparative Study of Classification Algorithms for Spam Email Data Analysis”,International Journal on Computer Science and Engineering (IJCSE) ,ISSN : 0975-3397
Vol. 3 No. 5 May 2011.
Shafi’i Muhammad Abdulhamid, Maryam Shuaib, Oluwafemi Osho, “Comparative Analysis of Classification Algorithms for Email Spam Detection “,I. J. Computer Network and Information Security, 2018, 1, 60-67 Published Online January 2018 in MECS (http://www.mecs-press.org/)DOI: 10.5815/ijcnis.2018.01.07.
Elifenesh Yitagesu Desta* and Tekalign Tujo Gurmessa ,“ Analysis and result of classification algorithm on email classification”,International Journal of Computer Engineering Research,SSN 2141-6494,Vol. 8(1), pp. 1-9, July-December 2019,
Quinlan JR. C4. 5: programs for machine learning. Morgan kaufmann; 1993.
Joachims T. A statistical learning learning model of text classification for support vector machines. In: Proceedings of the 24th annual international ACM SIGIR conference on research and
development in information retrieval, ACM; 2001. p. 128–36.
McCallum A, Nigam K. A comparison of event models for Naive Bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol. 752; 1998. p. 41–8.
I. H. Witten and F. Eibe, Data mining : practical machine learning tools and techniques, 2nd ed. Morgan Kaufmann Publishers, 2005.
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