Zhao Xiaoq-iang
College of Electrical Engineering an Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China
Yang Jia-min
College of Electrical Engineering an Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China
Zhou Jin-hu
College of Electrical Engineering an Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China
ABSTRACT
TANC (Tree Augmented Naive Bayes Classifier) is efficient extension of NBC (Naïve Bayes Classifier). This method not only inherits the simple and high efficiency performances of NBC, but also enhances the generalization ability. However it ignores the correlation between weaken attributes. So, an improved TANC method is proposed in this study according the dependence degree and the correlation between attributes. This method can set up a correspond dependencies to effectively improve the classification accuracy by selecting the appropriate attributes. Compared with NBC and TANC, experimental results showed this method is better than TANC and NBC in performance.
PDF References Citation
How to cite this article
Zhao Xiaoq-iang, Yang Jia-min and Zhou Jin-hu, 2013. An Improved TANC Method Based on Bayesian Equivalence Theorem. Information Technology Journal, 12: 4336-4339.
DOI: 10.3923/itj.2013.4336.4339
URL: https://scialert.net/abstract/?doi=itj.2013.4336.4339
DOI: 10.3923/itj.2013.4336.4339
URL: https://scialert.net/abstract/?doi=itj.2013.4336.4339
REFERENCES
- Deng, G., Y. Zhao, L. Liu and Y. Wang, 2012. An optimal bayes classification algorithm. Comput. Measurement Control, 20: 199-201.
Direct Link - Fan, K.X., 2009. Design of NB combination text classifier based on various feature selection. Comput. Eng., 35: 191-193.
Direct Link - Friedman, N. and D. Koller, 2003. Being bayesian about network structure: A bayesian approach to structure discovery in bayesian networks. Machine Learn., 50: 95-125.
CrossRef - Hsu, C.C., Y.P. Huang and K.W. Chang, 2008. Extended Naive Bayes classifier for mixed data. Expert Syst. Appli., 35: 1080-1083.
CrossRef - Perez, A., P. Larranga and I. Inza, 2009. Bayesian classifiers based on kernel density estimation: Flexible classifiers. Int. J. Approximate Reasoning, 50: 341-362.
CrossRef - Wang, Z.H. and F. Zhang, 2004. A selective tree-augmented network classifier based on rough set theory. J. Fudan Univ. (Nat. Sci.), 43: 725-728.
Direct Link