Second International Conference on Semantics, Knowledge, and Grid (SKG'06)
A NB-based approach to anti-spam application: DLB Classification Model
Guilin, Guangxi, China
November 01-November 03
ISBN: 0-7695-2673-X
Bei Hui, University of Electronic and Science Technology of China, China
Yue Wu, University of Electronic and Science Technology of China, China
Lin Ji, Sichuan University, China
Jia Chen, University of Electronic and Science Technology of China, China
Classification using Naive Bayesian (NB) classifier model, which is the context - based spam filter method, is a hot topic. The NB classifier is a simple and effective classifier, but its attribute independence assumption makes it unable to express its semantic relation. A new classification model is proposed that call Double level Bayesian classifier model (DLB). It not only considers the semantic dependence, but also has the simple and effective characters that are the advantages of NB classifier model. The conclusion we get from the experiment is that the performance using DLB classifier model is better than which using NB classifier model.
Citation:
Bei Hui, Yue Wu, Lin Ji, Jia Chen, "A NB-based approach to anti-spam application: DLB Classification Model," skg, pp.78, Second International Conference on Semantics, Knowledge, and Grid (SKG'06), 2006
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