A Novel Hybrid Bayesian Classification Model
一种新颖混合贝叶斯分类模型研究

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.