An SMS spam filtering system using support vector machine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6485 LNCS, Issue , 2010, Pages 577-584

  Joe, Inwhee ^a   Shim, Hyetaek ^a  

^a Hanyang University   (South Korea)

Author keywords

short messaging service; Spam filtering system; support vector machine; thesaurus

Indexed keywords

CHI-SQUARE STATISTICS; ISOLATED WORDS; PRE-PROCESSING; SAMPLE DATA; SHORT MESSAGING SERVICE; SPAM FILTERING; SVM(SUPPORT VECTOR MACHINE); WINDOWS ENVIRONMENT; CHI SQUARE STATISTIC;

INFORMATION TECHNOLOGY; INTERNET; SUPPORT VECTOR MACHINES; THESAURI; VECTORS; MESSAGE PASSING;

MESSAGE PASSING; SUPPORT VECTOR MACHINES;

EID: 78651069959     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17569-5_56     Document Type: Conference Paper

References (7)

1. 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)
2. 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)
3. 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)
4. Greenwood, P.E., Nikulin, M.S.: A Guide to Chi-Square Testing. Wiley Series in Probability and Statistics. Wiley-Interscience, Hoboken (2003)
5. Cortes, C., Vapnik, V.: Support vector network. Machine Learning 20, 273-297 (1995)
6. Sahay, S.: Support Vector Machines and Document Classification (2004)
7. http://nlp.kookmin.ac.kr/HAM/kor/index.html