CAJ | 학술논문

为提高朴素贝叶斯(Naive Bayesian)分类器的分类准确率,对朴素贝叶斯属性选择算法及假设属性概率值先验分布中的参数设置问题进行分析,提出将属性先验分布的参数设置加入到属性选择的过程中,并研究当先验分布服从Dirichlet分布及广义Dirichlet分布情况下的具体调整步骤.以UCI数据库为例进行仿真实验,结果表明当先验分布服从广义Dirichlet分布时,该方法可提高分类的准确率,如Parkinsons数据集,效率可提升13.32%.
위제고박소패협사(Naive Bayesian)분류기적분류준학솔,대박소패협사속성선택산법급가설속성개솔치선험분포중적삼수설치문제진행분석,제출장속성선험분포적삼수설치가입도속성선택적과정중,병연구당선험분포복종Dirichlet분포급엄의Dirichlet분포정황하적구체조정보취.이UCI수거고위례진행방진실험,결과표명당선험분포복종엄의Dirichlet분포시,해방법가제고분류적준학솔,여Parkinsons수거집,효솔가제승13.32%.
@@@@In order to improve the accuracy of the naive Bayesian classifier(NBC),the selective naive Bayesian (SNB)method and the attributes′ prior distribution are studied. A method for combining prior distribution and feature selection together is proposed,which finds out the best prior for each attribute after all attributes have been determined by the SNB algorithm. The experimental result on 10 data sets form UCI data repository shows that this method with the general Dirichlet prior generally achieves higher classification accuracy,such as the the efficiency of the data sets of Parkinson’s can be enhanced by 13.32%.