Abstract
Recently junk e-mail has been one of the most serious information overloading problems. This paper proposes multi-agent system to collaboratively filter spams from users’ mail stream. This multi-agent system is organized by personal agents automatically extracting features based on users’ manual filtering and facilitator managing knowledge extracted by personal agents. Especially, personal agents can analyze junk e-mails for extracting keyphrases and communicate with the others. Due to the domain specific properties of junk e-mail filtering we have formalized the features extracted from e-mail to be highly understandable and efficiently sharable. Thereby, we have defined two types of features in e-mail as apriori feature and keyphrase-based conceptual one. Besides, these features are integrated in the blackboard system of facilitator for collaborative learning. Finally, we show the filtering performance of collaborative learning by comparing with that of personal agent.
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References
Internet E-mail Corporate Usage Report. http://www.securitymanagement.com/library/worldtalk0200.html (2000)
Cohen, W.W.: Learning rules that classify e-mail. Proc. 1996 AAAI Spring Symposium on Machine Learning in Information Access (1996)
Sahami, M., Dumais, S., Heckerman, D., and Horvitz, E.: A bayesian approach to filtering junk e-mail. AAAI Workshop on Learning for Text Classification (1998)
Schapire, R. E., Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39 (2000) 135–168
Drucker, H., Wu, D., Vapnik, V. N.: Support Vector Machines for Spam Categorization. IEEE Transaction on Neural Networks, Vol. 10, No. 5 (1999)
Joachims, T.: A probabilistic analysis of the Ricchio algorithm with TFIDF for text categorization. Proceedings of 14th International Conference on Machine Learning (1997)
Quinlan, J.R.: C4.5: Programs for Machine Learning. San Mateo, Calif., Morgan Kaufmann Publishers (1993)
Maes, P.: Agents that Reduce Work and Information Overload. Communications of the ACM. 37(7) (1994) 31–40
Ollerenshaw, Z.: Spam, Spam, Spam, Spam.... Computer Fraud & Security, 2000(1) (2000) 13–14
Yu, B., Singh, M.P.: A social mechanism of reputation management in electronic communities. Proceedings of Fourth International Workshop on Cooperative Information Agents (2000) 154–165
Crocker, D.H.: Standard For The Format Of ARPA Internet Text Messages. ftp://ftp.rfc-editor.org/in-notes/rfc822.txt (1982)
Quinlan, J. R.: Induction of decision trees. Machine Learning 1(1) (1986) 81–106
Boone, G.: Concept Features in Re:Agent, an Intelligent Email Agent. Proceedings of the Second International Conference on Autonomous Agents (1998)
Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Information Retrieval 2(4) (2000) 303–336
Ferber, J.: Multi-Agent Systems-An Introduction to Distributed Artificial Intelligence. Addison-Wesley (1999)
Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition, 5(2) (1993) 199–220
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, N.Y., Addison-Wesley (1999) 29–30
JavaMailℳ API. http://java.sun.com/products/javamail/index.html
AgentBuilder. http://www.agentbuilder.com
Weiss, G. (ed.): Multiagent Systems-A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, Massachusetts London, England (1999)
Giraud-Carrier, C.: A Note on the Utility of Incremental Learning. AI Communications, 13(4) (2000) 215–223
Klusch, M. (ed.): Intelligent Information Agents-Agent-Based Information Discovery and Management on the Internet. Springer, Berlin (1999)
Jung, J. J., Yoon, J.-S., Jo, G.-S.: Collaborative Information Filtering by Using Categorized Bookmarks on the Web. In: Bartenstein, O., Geske, U., Hannebauer, M., Yoshie, O. (eds.): Web-Knowledge Management and Decision Support. Lecture Notes in Artificial Intelligence, Vol. 2543. Springer-Verlag, Berlin Heidelberg New York (2002) 243–257
UMBC KQML Web. http://www.cs.umbc.edu/kqml/
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Jung, J.J., Jo, GS. (2003). Collaborative Junk E-mail Filtering Based on Multi-agent Systems. In: Chung, CW., Kim, CK., Kim, W., Ling, TW., Song, KH. (eds) Web and Communication Technologies and Internet-Related Social Issues — HSI 2003. HSI 2003. Lecture Notes in Computer Science, vol 2713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45036-X_22
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DOI: https://doi.org/10.1007/3-540-45036-X_22
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