Architecture of adaptive spam filtering based on machine learning algorithms

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

Volumn 4494 LNCS, Issue , 2007, Pages 458-469

  Islam, Md Rafiqul ^a   Zhou, Wanlei ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

FP; Machine learning; NB; Spam; SVM

Indexed keywords

ALGORITHMS; ELECTRONIC MAIL; FEATURE EXTRACTION; LEARNING SYSTEMS; MATHEMATICAL MODELS;

FALSE POSITIVE (FP); LEGITIMATE EMAILS; SPAM FILTERING;

ADAPTIVE FILTERS;

EID: 37249055553     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72905-1_41     Document Type: Conference Paper

References (9)

1. Islam, R., Chowdhury, M., Zhou, W.: An Innovative Spam Filtering Model Based on Support Vector Machine. In: Proceedings of the IEEE International Conference on Intelligent Agents, Web Technologies and Internet Commerce 2, 348-353 (2005)
2. Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
3. Cohen, W., Singer, Y.: Context-sensitive learning methods for text categorization. ACM Transactions on Information Systems 17(2), 141-173 (1999)
4. Kaitarai, H.: Filtering Junk e-mail: A performance comparison between genetic programming and naïve bayes. Tech. Report, Department of Electrical and Computer Engineering, University of Waterloo (November 1999)
5. Androutsopoulos, I., et al.: Learning to filter spam e-mail: A comparison of a Naive Bayesian and a memory-based approach. In: Proceedings of the Workshop on Machine Learning and Textual Information Access, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases. Lyon, France, pp. 1-13 (2000)
6. Zhang, J., et al.: A Modified logistic regression: An approximation to SVM and its applications in large-scale text categorization. In: Proceedings of the 20th International Conference on Machine Learning, pp. 888-895. AAAI Press, California (2003)
7. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In Learning for Text Categorization. Papers from the Workshop, Madison, Wisconsin, AAAI Technical Report WS, pp. 98-105 (1998)
8. Drucker, H., Shahrary, B., Gibbon, D.C.: Support vector machines: relevance feedback and information retrieval. Inform. Process. Manag. 38(3), 305-323 (2003)
9. Huan, L., Lei, Y.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transaction on Knowledge and Data Engg. 17(4), 491-502 (2005)