An Assessment of Case-Based Reasoning for Spam Filtering

Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses Na

• Type: Journal
• Year: 2005
• Where: AIR
• Authors: Sarah Jane Delany, Padraig Cunningham, Lorcan Coyle
• Keywords: AIR 2005, Case-based Reasoning, Lazy Approach, Spam Filtering