SpamHunting

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. The work shows how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.