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.
| Published in | Machine Learning Research (Volume 1, Issue 1) |
| DOI | 10.11648/j.mlr.20160101.11 |
| Page(s) | 1-14 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Spam Filtering, Machine Learning Algorithms, SMS Spam
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APA Style
Hedieh Sajedi, Golazin Zarghami Parast, Fatemeh Akbari. (2016). SMS Spam Filtering Using Machine Learning Techniques: A Survey. Machine Learning Research, 1(1), 1-14. https://doi.org/10.11648/j.mlr.20160101.11
ACS Style
Hedieh Sajedi; Golazin Zarghami Parast; Fatemeh Akbari. SMS Spam Filtering Using Machine Learning Techniques: A Survey. Mach. Learn. Res. 2016, 1(1), 1-14. doi: 10.11648/j.mlr.20160101.11
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Download@article{10.11648/j.mlr.20160101.11,
author = {Hedieh Sajedi and Golazin Zarghami Parast and Fatemeh Akbari},
title = {SMS Spam Filtering Using Machine Learning Techniques: A Survey},
journal = {Machine Learning Research},
volume = {1},
number = {1},
pages = {1-14},
doi = {10.11648/j.mlr.20160101.11},
url = {https://doi.org/10.11648/j.mlr.20160101.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20160101.11},
abstract = {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.},
year = {2016}
}
TY - JOUR T1 - SMS Spam Filtering Using Machine Learning Techniques: A Survey AU - Hedieh Sajedi AU - Golazin Zarghami Parast AU - Fatemeh Akbari Y1 - 2016/12/05 PY - 2016 N1 - https://doi.org/10.11648/j.mlr.20160101.11 DO - 10.11648/j.mlr.20160101.11 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 1 EP - 14 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20160101.11 AB - 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. VL - 1 IS - 1 ER -
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