Spam filtering algorithm based on supervised Bayesian parameter estimation
Zhou Ming-tian · Journal of Computer Applications · 2006
To improve the reliability and completeness of spam filtering, the E-mail message format was carefully analyzed, and the spam characteristics were generalized and classified. Based on these analysis, a supervised Bayesian network for E-mail classifer was constructed. Parameter estimation on this network realized an uncertain inference to identify E-mail's sort. On-line learning for different E-mail testing sets shows that such a classifying network can ensure the classification and filtering efficiently. It practically provides a viable solution by building a supervised Bayesian classifying network to execute relatively complete characteristics learning and improve the accuracy of E-mail filtering.