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This paper proposes an incremental spam mail filtering using Naïve Bayesian classification which gives simplicity and adaptability. To keep the training set to a limited size and small, the sliding window is applied and the training set is updated when new emails are received. In effect, features in the training set are incrementally updated, and the(More)
Most content based spam filters are rule based or trained off-line. Handling new spam tactics is difficult and prone to high misclassification rate. This paper proposes an incremental adaptive spam mail filtering using Naïve Bayesian classification which gives good performance, simplicity and adaptability. We model an incremental scheme that receives(More)
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