Applied Research Of Bayesian Method On The Technology Of Anti-Spam

This dissertation is based on the "Narrowing the Digit-divide—West Program—Key Technique and Applied Research of the Public Information Platform based on the Domestic Linux " as the first batch of national 863 important special project (serial number: 2003AA1Z2530).With the rapid development of the Internet, Electronic mail brings both convenience and trouble to users, especially the later, for so much junk mail frequently appear in users' mailbox. How to filter these junk mails and retain useful e-mail is a big problem not only to the e-mail users but to the public information platform based on the domestic Linux and NC. This is the so-called "Anti-spam".In order to deal with the junk mail, we must adopt ways and methods from three aspects: lawmaking, organization and technology. In brief, it is a long hard fight between us and junk mail makers, just as the fighter of that of viruses, In this regard, the author has done some research of the theories and techniques of anti-spam, text filtering, Bayesian classifier model and the combination of multiple classifiers.Beyesian classifier algorithm is a filtering method based on the theory of statistical probability. It shows fairly satisfactory performance on the areas of text classification. Accordingly, the author proceeds a further research on Naive Beyesian classifier(NBC),Boosted NBC, Semi-NBC(SNBC),Tree-Augmented Naive Bayesian Classifier (TAN),Increased NBC and Bayesian Netwok(BN).Based on these researches, the author focuses on establishing the Bayesian multiple classifiers optimization algorithm on anti-spam. He also explore the improved threshold method in the anti-spam model based on Bayesian Classifier.Experimental results show that this new algorithm can achieve fairly satisfactory performance in the mail filtering applications and may provide solid theoretical support for designing the anti-spam software.