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《Journal of Hunan University of Arts and Science(Science and Technology)》 2008-01
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A selected and weighted naive bayes classifier

WANG Jun1,LIU Huai-Sheng 2 (1.Huainan normal university,Huainan,Anhui,232001;2.Gas utilization branch Huainan mining group corporation,Huainan, Anhui,232001)  
Naive Bayesian classifier is a simple and effective classifier,but its attribute independence assumption makes it unable to express the dependence among attributes,and affects its classification accuracy.Weighted naive Bayes is it extension.On the basis of analyzing the evaluation of condition attribute with correlation and attribute reduction and selecting a set of more independence attribute, The present paper presents SWNBC(A Selected and Weighted Naive Bayes Classifier)which combined the merits of SNBC and WNBC.Compared with Naive Bayesian Classification Model,experimental results show EANBC has higher accuracy.
【Fund】: 安徽省高等学校省级自然科学研究项目(KJ2007B075)
【CateGory Index】: TP18
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【Citations】
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【Co-citations】
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1 WANG Jun(Huainan Normal College,Huainan 232001,China);A Naive Bayesian Classification Model Based on the Measurement of Attribute with Correlation[J];Journal of Anqing Teachers College(Natural Science Edition);2007-02
2 CHEN Jingnian1,2,HUANG Houkuan1,YANG Liping1,2,TIAN Fengzhan1 (1.School of Computer and Information Technology,Beijing Jiaotong University, Beijing 100044,China;2.Dept.of Information and Computing Science,Shandong University of Finance,Jinan,250014,China);Distribution-Based Selective Classifiers for Incomplete Data[J];Journal of Beijing Jiaotong University;2008-02
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