International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 2, February 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Comparative Study On Supervised Machine Learning Algorithms For Spam Mail Detection

[Full Text]



C.Nalini, R.Shantha Kumari,J.Sudeeptha



Spam mail,Random Forest, Navie-bayes,k-NN,SVM,DT, Ensemble learning algorithm



Electronic mail (E-mail) is used to exchange messages between people via internet. E-mail protocols like Simple Mail Transfer Protocol (SMTP), POP (Post Office Protocol) and IMAP (Internet Message Access Protocol) are used to transfer messages from sender to receiver. Due to the flaws in E-mail protocols, development of online businesses and advertisement companies create E-mail based intimidation. E-mail spam is called as junk mail. Today handling spam mail is one of the major problems in software companies. Since spam mail causes traffic problems and bottle necks that limit memory space, computing power and speed. And also a user has to spend more time to detect and obliterate spam mails. Machine learning models are used to are used to overcome this problem. Machine learning models are categorized into supervised, unsupervised and semi supervised learning models. Supervised learning models are used to classify E-mails, filter and prevent the spam mail. The proposed work explores the performance of machine learning algorithms like Decision Tree(DT), Navie-bayes, k-Nearest Neighbours (k-NN),Support Vector Machines(SVM) and Random Forest(RF) learning algorithms for classifying spam messages from E-mail. Accuracy, F-measure and recall parameters are used to evaluate the performance of the learning algorithms.



[1] D. Puniškis, R.Laurutis and R. Dirmeikis “An Artificial Neural Nets for Spam
[2] e-mail Recognition”, Electronics and electrical engineering, Vol. 69, No. 5, pp. 73 – 76, 2006.
[3] Patil, T. and Sherekar, S. ”Performance Analysis of Navie Bayes and Classification Algorithm for Data Classification", International Journal Of Computer Science And Applications, 2013.
[4] W. Li, N. Zhong, Y. Yao, J. Liu, C. Liu, “Spam filtering and email-mediated applications”, International Workshop on Web Intelligence Meets Brain Informatics, 2006.
[5] A. Bhowmick, S.M. Hazarika, “Machine Learning for E-Mail Spam Filtering: Review, Techniques and Trends”, arXiv:1606.01042v1 [cs.LG] 3 Jun 2016, 2016, pp.1–27.