Mail Filtering Based On Dual Membership Fuzzy Support Vector Machine

With the rapid development of Internet, E-mail has become Primary means in modern telecommunication. However, Spams(also named as"junk mails") simultaneously pervade widespread online, bringing a lot of troubles to numerous users. Junk emails(spams)are becoming an increasingly serious security problem, attracting attentions from both the research community and the general populace. Therefore, it is important and practieal to prevent and control spam effectively.The thesis, on the one hand, investigates thoroughly considerable anti-spam documents and data from both home and abroad.Furthermore, analysis and conclusion are made on existing anti-spam techniques. Nowadays, anti-spam measures commonly include black or white list technology, manual rules and keyword based content filtering. Another approach is using automated text categorization and information filtering to filter spam.An e-mail filtering system can learn directly from a user's mail set.We find that the current machine learning methods classify emails into the legitimate or the spam for a certainty. However, in practice different users of server-side hold different opinions of whether an email is the legitimate or not, and to what extent.As a sult, research of email filtering should be considered as dealing with the uncertainties.Based on the fuzzy of information in mails and the asymmetry of misjudgment price in legitimate mails and spam, a mail filtering method is proposed. It makes use of dual membership fuzzy support vector machine. A new and more effective fuzzy membership as a function of define sample's membership degree is proposed. It used for the measurement of the membership degree of samples. According to providing a different dual membership for each sample, a dual membership fuzzy support vector machine classifier is derived.It improves the accuracy of mail filtering. The simulation results and comparative methods test show that the method is able to effectively reduce the misjudgment of legitimate mails as spam. In additional, it has a high accuracy and so on.