Naive Bayes spam filtering using word-position-based attributes

2nd Conference on Email and Anti-Spam

Volumn , Issue , 2005, Pages

  Hovold, Johan ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFIERS; ELECTRONIC MAIL; LEARNING ALGORITHMS; LEARNING SYSTEMS;

ATTRIBUTE VECTORS; CLASSIFICATION PERFORMANCE; COST-SENSITIVE CLASSIFICATION; NAIVE BAYES; NAIVE BAYES CLASSIFIERS; SPAM FILTER; SPAM FILTERING; WEIGHTING SCHEME;

INTERNET;

EID: 84888986410     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000). An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 160-167). Athens, Greece.
2. Androutsopoulos, I., Paliouras, G., & Michelakis, E. (2004). Learning to filter unsolicited commercial e-mail (Technical Report 2004/2). NCSR "Demokritos". Revised version.
3. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Wiley.
4. Joachims, T. (1997). A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Proceedings of ICML-97, 14th International Conference on Machine Learning (pp. 143-151). Nashville, US.
5. McCallum, A., & Nigam, K. (1998). A comparison of event models for naive bayes text classification. Learning for Text Categorization: Papers from the 1998 Workshop (pp. 41-48). Madison, Wisconsin: AAAI Technical Report WS-98-05.
6. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
7. Sahami, M., Dumais, S., Heckerman, D., & Horvitz, E. (1998). A bayesian approach to filtering junk E-mail. Learning for Text Categorization: Papers from the 1998 Workshop (pp. 55-62). Madison, Wisconsin: AAAI Technical Report WS-98-05.