Spam detection using character n-grams

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

Volumn 3955 LNAI, Issue , 2006, Pages 95-104

  Kanaris, Ioannis ^a   Kanaris, Konstantinos ^a   Stamatatos, Efstathios ^a  

^a UNIVERSITY OF THE AEGEAN   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; LEARNING SYSTEMS; PROBLEM SOLVING; SPAMMING;

ANTI-SPAM FILTERS; SPAM DETECTION; SUPPORT VECTOR MACHINES; TERM-FREQUENCY REPRESENTATIONS;

INFORMATION ANALYSIS;

EID: 33745834760     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11752912_12     Document Type: Conference Paper

References (17)

1. Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1) (2002) 1-47
2. Sahami M., Dumais S., Heckerman D., Horvitz E.: A Bayesian Approach to Filtering Junk E-mail. In Proc. of AAAI Workshop on Learning for Text Categorization (1998).
3. Androutsopoulos I., Koutsias J., Chandrinos K.V., Paliouras G., Spyropoulos C.D.: An Evaluation of Naive Bayesian Anti-Spam Filtering. In Potamias, G., Moustakis, V. and van Someren, M. (Eds.), Proc. of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning (2000) 9-17
4. Sakkis, G., Androutsopoulos I., Paliouras G., Karkaletsis V., Spyropoulos C.D., Stamatopoulos P.: A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists. Information Retrieval, 6(1) (2003) 49-73
5. Drucker, H., Wu, D., Vapnik, V.: Support Vector Machines for Spam Categorization. IEEE Trans Neural Network, 10 (1999) 1048-1054
6. Androutsopoulos I., Paliouras G., Michelakis E.; Learning to Filter Unsolicited Commercial E-Mail. Technical report 2004/2, NCSR "Demokritos" (2004)
7. Cavnar, W., Trenkle, J.: N-gram-based text categorization. In Proc. 3rd Int'l Symposium on Document Analysis and Information Retrieval (1994) 161-169
8. Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based Author Profiles for Authorship Attribution, In Proc. of the Conference Pacific Assoc. Comp. Linguistics (2003)
9. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text Classification Using String Kernels The Journal of Machine Learning Research, 2 (2002) 419-444
10. Vapnik V.; The Nature of Statistical Learning Theory, Springer, New York (1995).
11. Joachims T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proc. of the European Conference on Machine Learning (1998)
12. Sakkis G., Androutsopoulos I., Paliouras G., Karkaletsis V., Spyropoulos C.D., Stamatopoulos P.: Stacking Classifiers for Anti-Spam Filtering of E-Mail, In Proc. of 6th Conf. Empirical Methods in Natural Language Processing (2001) 44-50
13. Hovold J.: Naive Bayes Spam Filtering Using Word-Position-Based Attributes. In Proc. of the Second Conference on Email and Anti-Spam (2005)
14. Yang, Y., Petersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In Proc. of the 14th Int. Conference on Machine Learning (1997) 412-420
15. Pampapathi, R., Mirkin, B., Levene, M.: A Suffix Tree Approach to Text Categorisation Applied to Spam Filtering, http://arxiv.org/abs/cs.AI/0503030
16. Berger, H., Koehle, M., Merkl, D.: On the Impact of Document Representation on Classifier Performance in e-Mail Categorization. Proc. of the 4th International Conference on Information Systems Technology and its Applications (2005) 19-30
17. Witten I.H., Frank E.; Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmann, San Francisco (2000)