C4.5: programs for machine learning
C4.5: programs for machine learning
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Agents that reduce work and information overload
Communications of the ACM
Concept features in Re:Agent, an intelligent Email agent
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet
Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Modern Information Retrieval
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Social Mechanism of Reputation Management in Electronic Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
A note on the utility of incremental learning
AI Communications
Collaborative information filtering by using categorized bookmarks on the web
INAP'01 Proceedings of the Applications of prolog 14th international conference on Web knowledge management and decision support
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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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.