基于多重判别分析的朴素贝叶斯分类器
Naive Bayesian Classifier Based on Multiple Discriminant Analysis
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摘要: 通过分析朴素贝叶斯分类器的分类原理，并结合多重判别分析的优点，提出了一种基于多重判别分析的朴素贝叶斯分类器DANB（Discrim inantAnalysis Naive Bayesian classifier）．将该分类方法与朴素贝叶斯分类器（Naive Bayesian classifier，NB）和TAN分类器（Tree Augm ented Naive Bayesian classifier）进行实验比较，实验结果表明在大多数数据集上，DANB分类器具有较高的分类正确率．Abstract: On the basis of analyzing the classification principle of naive Bayesian classifier and integrating the advantages of multiple discriminant analysis,a naive Bayesian classifier based on multiple discriminant analysis,DANB(Discriminant Analysis Naive Bayes),is proposed.The DANB classifier is compared with Naive Bayes(NB) classifier and TAN(Tree Augmented Naive Bayes) classifier by an experiment.Experiment results show that this model has higher classification accuracy in most datasets.
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