loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
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
ASCII Text
x 
 
Bei Hui, Yue Wu, Lin Ji, Jia Chen, "A NB-based approach to anti-spam application: DLB Classification Model," Semantics, Knowledge and Grid, International Conference on, pp. 78, Second International Conference on Semantics, Knowledge, and Grid (SKG'06), 2006.
 
 
BibTex
x
 
@article{ 10.1109/SKG.2006.10,
author = {Bei Hui and Yue Wu and Lin Ji and Jia Chen},
title = {A NB-based approach to anti-spam application: DLB Classification Model},
journal ={Semantics, Knowledge and Grid, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2673-X},
pages = {78},
doi = {http://doi.ieeecomputersociety.org/10.1109/SKG.2006.10},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Semantics, Knowledge and Grid, International Conference on
TI - A NB-based approach to anti-spam application: DLB Classification Model
SN - 0-7695-2673-X
SP
EP
A1 - Bei Hui,
A1 - Yue Wu,
A1 - Lin Ji,
A1 - Jia Chen,
PY - 2006
KW - null
VL - 0
JA - Semantics, Knowledge and Grid, International Conference on
ER -
 
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
Usage of this product signifies your acceptance of the Terms of Use.