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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]

 

AUTHOR(S)

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

 

KEYWORDS

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

 

ABSTRACT

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.

 

REFERENCES

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[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.