A spam SMS filtering method based on minimum risk Bayesian decision-making is studied. For SMS messages that are mainly text messages, the information gain method is used for feature selection, and the minimum risk Bayesian decision method is used for classification. The method is experimented with a self-built SMS corpus. The experimental results show that this method can accurately classify SMS messages and reduce the classification error rate of legitimate SMS messages. The classification accuracy rate reaches 99.3%, which meets the requirements of SMS classification. Keywords: spam SMS; SMS filtering; text classification; naive Bayes
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