Email Spam Detection using Ensemble Methods
Uma Bhardwaj1, Priti Sharma2
1Uma Bhardwaj, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
2Priti Sharma, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
Manuscript received on 16 August 2019. | Revised Manuscript received on 21 August 2019. | Manuscript published on 30 September 2019. | PP: 4148-4153 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5485098319/2019©BEIESP | DOI: 10.35940/ijrte.C5485.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The swiftly growth of spam email has escalated the need to upgrade the existing spam detection and filtration methods. There is the existence of several machine learning methods for the classification and detection of email spam but these lacks in some cases. In this research work ensemble methods are adapted to detect the email spam. The machine learning methods of Multinomial Naïve Bayes and J48 Decision Tree algorithms are considered and ensembled. The considered ensemble methods are bagging and boosting. The experimentation is conducted on the dataset of CSDMC2010 Spam corpus. The results for the considered dataset are evaluated using individual classifiers, bagging, and boosting ensemble approaches. The system performance is accessed in terms of precision, recall, f-measure, and accuracy. The experimental outcomes indicates the distinguish results for the detection of email spam using ensemble methods.
Keywords: Email Spam Detection, Ensemble Methods, Bagging, Boosting, Multinomial Naïve Bayes, and J48 Decision Tree.
Scope of the Article: Decision Making