Spam Filtering using SVM with different Kernel Functions
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10.5120/ijca2016908395 |
Deepak Kumar Agarwal and Rahul Kumar. Article: Spam Filtering using SVM with different Kernel Functions. International Journal of Computer Applications 136(5):16-23, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX
@article{key:article,
author = {Deepak Kumar Agarwal and Rahul Kumar},
title = {Article: Spam Filtering using SVM with different Kernel Functions},
journal = {International Journal of Computer Applications},
year = {2016},
volume = {136},
number = {5},
pages = {16-23},
month = {February},
note = {Published by Foundation of Computer Science (FCS), NY, USA}
}
Abstract
The growing volume of unwanted bulk e-mail (also known as junk-mail or spam) has generated a need for trustworthy anti-spam filters. Now a day, many Machine learning techniques have been used which are robotically filter the junk e-mail in much unbeaten rate. In this paper, we used one of the most popular machine learning Algorithm support vector machine (SVM) with different parameters using different kernel-functions (linear, polynomial, RBF, sigmoid) are implemented on spambase-dataset. Comparison of SVM performance for all kernels (linear, polynomial, RBF, sigmoid) using different parameters (C-SVC, NU-SVC) evaluated on spambase-dataset to get best accuracy.
References
- Cormack, Gordon. Smucker, Mark. Clarke, Charles " Efficient and effective spamfiltering and re-ranking for large web datasets" Information Retrieval, Springer Netherlands. January 2011
- Muhammad N. Marsono, M. Watheq El-Kharashi, Fayez Gebali “Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification” Elsevier Computer Networks, 2009.
- KwangLeng Go h, Ashutosh Kumar Singh, “Comprehensive Literature Review on Machine Learning structures for Web Spam Classification”, 4thInternational Conference o n Eco-friendly Computing and Communication Systems (ICECC S),Procedia Computer Science 70 (2015) 434 – 441
- Xuchun Li , Lei Wang, Eric Sung,” AdaBoost with SVM-based component classifiers”, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Engineering Applications of Artificial Intelligence 21 (2008) 785–795.
- Thiago S. Guzella *, Walmir M. Caminhas,” A review of machine learning approaches to Spam filtering”, Department of Electrical Engineering, Federal University of Minas Gerais, Ave. Antonio Carlos, 6627, Belo Horizonte (MG) 31270-910, Brazil, Expert Systems with Applications 36 (2009) 10206–10222.
- Kim Janssensa,*, NicoNijstena, Robrecht Van Goolena, “Spam and Marketing Communications”, Enterprise and the Competitive Environment 2014 conference, ECE 2014, 6–7 March 2014, Brno, Czech Republic, Procedia Economics and Finance 12 (2014) 265 – 272
- Mohammed N. Al-Kabi a, Izzat M. Alsmadi b,*, Heider A. Wahsheh c,” Evaluation of Spam Impact on Arabic Websites Popularity”, Journal of King Saud University – Computer and Information Sciences (2015) 27, 222–229
- http://www.ics.uci.edu/~mlearn/MLRepository.html
- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvmWu, C. ”Behavior-based spam detection using a hybrid method of rule-based techniques andneural networks” Expert Syst., 2009
Keywords
Spam-filtering, Support Vector Machine, Kernel-functions