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1、The aim of this thesis is to improve accuracy of Bayesian spam filtering, the most popular and widely used approach in spam filtering. Among the various possible approaches to this aim, two approaches that improved the
2、filtering performances arepresented in this thesis. Three popular evolutions of Bayesian spam filtering algo rithms: Naive Bayes, Paul Graham's and Gary Robinson's are reviewed. Formulated on top of those evolutions, pr
3、oposed algorithms incorporate new novel ideas. The first approach proposed is co-weighting of multiple probability estimations. Though based on Bayesian theorem, several ways of computing probability estima tions hav
4、e been proposed and used. Those estimations are examined and a new,combined, more effective estimation based on co-weighted multi-estimations is pro posed. The approach is compared with individual estimations. The se
5、cond approach is based on co-weighted multi-area information. Bayesian spam filters, in general, compute probability estimations for tokens either without considering the email areas of occurrences except the body or tre
6、ating the same token occurred in different areas as different tokens. However, in reality the same token occurring in different areas are inter-related and the relation too could play role in the classific ation. This n
7、ovel idea is incorporated, co-relating multi-area information by co-weighting them and obtaining more effective combined integrated probability estimations for tokens. It is shown that this approach also improves the pe
8、rformance of spam filtering. The new approach is compared with individual area-wise estimations and traditional separate estimations in all areas. The filters are tested by thorough experiments with three well known
9、public cor pora: Ling Spam, Spam Assassin and Annexia/Xpert and they are evaluated using several performance measures. Both the proposed approaches are shown to exhibit significant improvement, stability, robustness and
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