Machine Learning
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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This paper presents a system for automatically detecting and filtering unsolicited electronic messages. The underlying hybrid filtering method is based on e-mail origin and content. The system classifies each of the three parts of e-mails separately by using a sinole Bayesian filter together with a heuristic knowledge base. The system extracts heuristic knowledge from a set of labelled words as the basis on which to begin filtering instead of conducting a training stage using a historic body of pre-classified e-mails. The classification resulting from each part is then integrated to achieve optimum effectiveness. The heuristic knowledge base allows the system to carry out intelligent management of the increase in filter vocabularies and thus ensures efficient classification. The system is dynamic and interactive and the role of the user is essential to keep the evolution of the system up to date by incremental machine learning with the evolution of spam. The user can interact with the system over a customized, friendly interface, in real time or at intervals of the user’s choosing.