Collaborative Junk E-mail Filtering Based on Multi-agent Systems

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.