Abstract
This paper describes a powerful and adaptive spam filtering system for SMS (Short Messaging Service) that uses SVM (Support Vector Machine) and a thesaurus. The system isolates words from sample data using a pre-processing device and integrates meanings of isolated words using a thesaurus, generates features of integrated words through chi-square statistics, and studies these features. The system is realized in a Windows environment and its performance is experimentally confirmed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: The Fourteenth International Conference on Machine Learning (ICML 1997), pp. 412–420. Morgan Kaufmann, San Francisco (1997)
Schutze, H., Hull, D., Pedersen, J.: A comparison of classifiers and document representations for the routing problem. In: International ACM SIGIR conference on research and development in information retrieval (1995)
Yang, Y., Wilbur, J.: Using corpus statistics to remove redundant words in text categorization. Journal of the American Society of Information Science 47(5) (1996)
Greenwood, P.E., Nikulin, M.S.: A Guide to Chi-Square Testing. Wiley Series in Probability and Statistics. Wiley-Interscience, Hoboken (2003)
Cortes, C., Vapnik, V.: Support vector network. Machine Learning 20, 273–297 (1995)
Sahay, S.: Support Vector Machines and Document Classification (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Joe, I., Shim, H. (2010). An SMS Spam Filtering System Using Support Vector Machine. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-17569-5_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17568-8
Online ISBN: 978-3-642-17569-5
eBook Packages: Computer ScienceComputer Science (R0)