Collaborative Junk E-mail Filtering Based on Multi-agent Systems.
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.
Conference Paper: Towards Collaborative Spam Filtering Based on Collective Intelligence.[Show abstract] [Hide abstract]
ABSTRACT: It is important to make spam mail filters more intelligent.This paper proposes a collective intelligence-based approach to collaboratively build an unified knowledge by a cooperative multi-agent system (MAS), which is composed of a facilitating agent and a number of personal agents. The aim of this work is to support each personal agent to automatically filter spam mails from its mail box as referring to the centralized knowledge. The whole process is composed of two steps; i) personal agents can learn userpsilas actions by automatic feature extraction from the spams, and then, ii) they can communicate with the other agents to exchange the features. Due to domain-specific properties of the spam mail filtering, we have tried to formalize the features extracted from e-mails by an agent, so that it can be highly understandable and efficiently sharable with other agents. In particular, we have defined two types of features from the spam mails; i) field features, and ii) concept features based on keyphrases. Moreover, facilitator organizes hierarchical cluster structure to manage knowledge from these agents. Finally, we show the filtering performance of collaborative learning by comparing with personal agent.First Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009, Dong hoi, Quang binh, Vietnam, April 1-3, 2009; 01/2009
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ABSTRACT: On the heterogeneous web information spaces, users have been suffering from efficiently searching for relevant information. This paper proposes a mediator agent system to estimate the semantics of unknown web spaces by learning the fragments gathered during the users' focused crawling. This process is organized as the following three tasks; (i) gathering semantic information about web spaces from personal agents while focused crawling in unknown spaces, (ii) reorganizing the information by using ontology alignment algorithm, and (iii) providing relevant semantic information to personal agents right before focused crawling. It makes the personal agent possible to recognize the corresponding user's behaviors in semantically heterogeneous spaces and predict his searching contexts. For the experiments, we implemented comparison-shopping system with heterogeneous web spaces. As a result, our proposed method efficiently supported the users, and then, network traffic was also reduced.Information Retrieval 04/2007; 10(1):85-109. · 0.63 Impact Factor
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ABSTRACT: CAFE (collaborative agents for filtering e-mails) is a multi-agent system to collaboratively filter spam and classify legitimate messages in users' mail stream. CAFE associates a proxy agent with each user, and this agent represents a sort of interface between the user's e-mail client and the e-mail server. With the support of other types of agents, the proxy agent makes a classification of new messages into three categories: ham (good messages), spam and spam-presumed. Ham messages can be in their turn divided on the basis of the sender's identity and reputation. The reputation is collaboratively inferred from users' ratings. The filtering process is performed using three kinds of approach: a first approach based on the usage of an hash function, a static approach using DNSBL (DNS-based black lists) databases and a dynamic approach based on a Bayesian filter. We give a mathematical representation of the system, showing that if users collaborate, the fault probability decreases in proportion to the number of active usersCollaborative Computing: Networking, Applications and Worksharing, 2005 International Conference on; 01/2005