Volume 1, Issue 1, December 2016, Page: 1-14
SMS Spam Filtering Using Machine Learning Techniques: A Survey
Hedieh Sajedi, Dept. of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
Golazin Zarghami Parast, Dept. of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
Fatemeh Akbari, Dept. of Electrical, Computer and Information Technology, Islamic Azad University, Tehran, Iran
Received: Sep. 28, 2016;       Accepted: Nov. 5, 2016;       Published: Dec. 5, 2016
DOI: 10.11648/j.mlr.20160101.11      View  7331      Downloads  723
Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library. Study selection: Those articles dealing with machine learning and hybrid approaches for SMS spam filtering. Data extraction: Many articles extracted by searching a predefined string and the outcome was reviewed by one author and checked by the second. The primary paper was reviewed and edited by the third author. Results: A total of 44 articles were selected which were concerned machine learning and hybrid methods for detecting SMS spam messages. 28 methods and algorithms were extracted from these papers and studied and finally 15 algorithms among them have been compared in one table according to their accuracy, strengths, and weaknesses in detecting spam messages of the Tiago dataset of spam message. Actually, among the proposed methods DCA algorithm, the large cellular network method and graph-based KNN are three most accurate in filtering SMS spams of Tiago data set. Moreover, Hybrid methods are discussed in this paper.
Spam Filtering, Machine Learning Algorithms, SMS Spam
To cite this article
Hedieh Sajedi, Golazin Zarghami Parast, Fatemeh Akbari, SMS Spam Filtering Using Machine Learning Techniques: A Survey, Machine Learning Research. Vol. 1, No. 1, 2016, pp. 1-14. doi: 10.11648/j.mlr.20160101.11
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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