A Case-Based Approach to Spam Filtering that Can Track Concept Drift
نویسندگان
چکیده
There are a few key benefits of a case-based approach to spam filtering. First, the many different sub-types of spam suggest that a local learner, such as Case-Based Reasoning (CBR) will perform well. Second, the lazy approach to learning in CBR allows for easy updating as new types of spam arrive. Third, the case-based approach to spam filtering allows for the sharing of cases and thus a sharing of the effort of labeling email as spam. In this paper we introduce a case-based approach to spam filtering and present preliminary evidence of the first two of these advantages.
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