%0 Journal Article
%T A Novel Hybrid Bayesian Classification Model<br>一种新颖混合贝叶斯分类模型研究
%A LI Xu-Sheng
%A GUO Yao Huang
%A <br>李旭升
%A 郭耀煌
%J 计算机科学
%D 2006
%I 
%X Naive Bayesian classifier (NB) is a simple and effective classification model,but it is unable to make the best of the information of the training dataset,thus affecting its classification performance.On the basis of analyzing the classification principle of NB and integrating strongpoint of Linear Diseriminant Analysis (LDA) and Kernel Discrimi- nant Analysis (KDA),a new hybrid Bayesian classification model,DANB (Discriminant Analysis Naive Bayesian clas- sifier),is proposed.DANB classifier is compared with NB and TAN (Tree Augmented Naive Bayesian classifier) by an experiment.Experiment results show that this model has higher classification accuracy in most datasets.
%K Naive Bayesian classifier
%K Linear discriminant analysis
%K Kernel discriminant analysis
%K TAN classification<br>朴素贝叶斯分类器
%K 线性判别分析
%K 核判别分析
%K TAN分类器
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=797EBFE036FAC77A&yid=37904DC365DD7266&vid=27746BCEEE58E9DC&iid=9CF7A0430CBB2DFD&sid=5E25104E99903E8A&eid=12DC19455C3A2FA8&journal_id=1002-137X&journal_name=计算机科学&referenced_num=2&reference_num=15