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《Journal of Jilin University(Information Science Edition)》 2007-01
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Mixed Naive Bayes Classifier Model

DONG Li-yan~(1a),LIU Guang-yuan~(1b),YUAN Sen-miao~(1a),LI Yong-li~2,SUN Ming-hui~(1a)(1a.College of Computer Science and Technology,Jilin University,Changchun 130012,China;1b.College of Communication Engineering,Jilin University,Changchun 130022,China;2.College of Computer,Northeast Normal University,Changchun 130024,China)  
In order to decrease the attribute independence assumption which is made by Naive Bayesian,a new Bayesian model MBN(Mixed Naive Bayes) is introduced.It divides attribute sets into several independent subsets by analyzing Bayesian theorem. The subsets are trained by TAN(Tree Augmented Naive Bayes) and the results are integrated by formula.MBN classifier is compared with Naive Bayes classifier and TAN classifier by an experiment.Experimental results show that this model has higher classification accuracy in most data sets.
【Fund】: 国家自然科学基金资助项目(60275026)
【CateGory Index】: TP183;TP311.13
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