Incremental learning based on interactive spam filter
Tran Nhat Quang · Journal of Tsinghua University(Science and Technology) · 2006
An interactive spam filter was developed to reduce misclassification rates when filtering spam.A set of weighted rules is used to filter spam with the weights selected using an improved genetic algorithm.The false positive and false negative rates are improved using user feedback on the misclassified information with incremental learning to dynamically adjust the rule weights.The filtering method was implemented by expanding SpamAssassin with tests on an email server at CCERT(Cernet Computer Emergency Response Term) in Tsinghua University.Test results show that the method effectively reduces misclassifications without affecting spam filtering quality.