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  2015, Vol. 28 Issue (6): 499-506    DOI: 10.16451/j.cnki.issn1003-6059.201506003
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Learning and Ensemble of One-Order Bayesian Derivative Classifiers with Continuous Attributes
WANG Shuang-Cheng1,2, GAO Rui1,3, LENG Cui-Ping1
1.School of Mathematics and Information Science, Shanghai Lixin University of Commerce, Shanghai 210620
2.Lixin Accounting Research Institute, Shanghai Lixin University of Commerce, Shanghai 210620
3.School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433

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Abstract  

Considering the efficiency and reliablity, Gaussian copula function, Gaussian kernel function with smoothing parameter, the criteria of classification accuracy and the greedy selection of attribute parent node are combined to carry out the learning, optimization and ensemble of one-order Bayesian derivative classifiers with continuous attributes. Experiment and analysis are executed by using datasets with continuous attributes in UCI dataset. The results show that the classifiers after optimization and ensemble have good classification accuracy.

Key wordsBayesian Derivative Classifier      Gaussian Copula Function      Classifier Ensemble      Bayesian Network      Continuous Attribute     
Received: 28 November 2013     
ZTFLH: TP181  
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WANG Shuang-Cheng
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Cite this article:   
WANG Shuang-Cheng,GAO Rui,LENG Cui-Ping. Learning and Ensemble of One-Order Bayesian Derivative Classifiers with Continuous Attributes[J]. , 2015, 28(6): 499-506.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201506003      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2015/V28/I6/499
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