Mail classification method combining user relationships with Bayers theory

Abstract

The invention discloses a mail classification method combining user relationships with the Bayers theory. According to the method, through extracting user relationships contained in mails to construct a user relationship diagram and combining the Naive Bayes method, automatic classification of the electronic mails is realized, an accuracy rate of a classification system is improved, and a misjudgment rate is reduced. Through the method, a confidence factor is proposed to estimate credibility of a classification result of a Naive Bayes classifier, the Naive Bayes method is combined with the user relationship diagram, the user relationships contained in the normal mails are utilized to construct the user relationship diagram, and a user white list is generated according to general mail processing habit rules of users. In a new mail classification process, classification results are continuously fed back to the user relationship diagram, the user white list is further updated, so the user relationship diagram and the user white list are automatically adjusted by the classification system according to change of the new mails, and thereby the higher accuracy rate is realized.

Description

technical field [0001] The invention belongs to the technical field of data mining, and more specifically relates to a mail classification method combining user relationship and Bayesian theory. Background technique [0002] With the rapid development of the Internet today, people's daily life has been integrated with the network environment. More and more people use the Internet for office, shopping, consumption, entertainment and other activities, among which email (E-mail) has become a daily One of the important means of communication. According to the 35th "Statistical Report on Internet Development in China" released by China Internet Network Information Center (CNNIC) in February 2015, as of December 2014, the number of Chinese netizens exceeded 649 million. More than 251 million. In foreign countries, there were about 929 million business email accounts in 2013, and it is still growing. However, problems also follow. A large number of spam mails are flooding people...

AI Technical Summary

Problems solved by technology

[0026] Aiming at the above defects or improvement needs of the prior art, the present invention provides a mail classification method combining user relationship and Bayesian theory. The technical problems of low classification accuracy and high misjudgment rate of normal emails, combined the user relationship contained in the emails with the Naive Bayesian method to improve the accuracy of mail classification and reduce the misjudgment rate of mails

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