引用本文: 王蓓,孙玉东,金晶,等.基于D-vine Copula理论的贝叶斯分类器设计[J].控制与决策,2019,34(6):1319-1324
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 本文已被：浏览 98次   下载 169次 码上扫一扫！ 分享到： 微信 更多 字体:加大+|默认|缩小- 基于D-vine Copula理论的贝叶斯分类器设计 王蓓1, 孙玉东1, 金晶1, 张涛2, 王行愚1 (1. 华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海200237;2. 清华大学自动化系，北京100084)

DOI：10.13195/j.kzyjc.2017.1589

Bayesian classifier based on D-vine Copula theory
WANG Bei1,SUN Yu-dong1,JIN Jing1,ZHANG Tao2,WANG Xing-yu1
(1. Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education,East China University of Science and Technology,Shanghai200237,China;2. Department of Automation,Tsinghua University,Beijing100084,China)
Abstract:
In the traditional Bayesian classifiers such as the Gaussian discriminant analysis method and the Naive Bayesian method, the correlation between variables are commonly simplified when constructing the joint probability distribution of variables. Accordingly, the estimation of the class conditional probability density would have differences with the actual data. In this study, a Bayesian classifier based on the D-vine Copula theory is developed by investigating on the correlation between variables. The main objective is to improve the accuracy of the class conditional probability density estimation. The joint probability distribution of variables is decomposed into a series of pair Copula functions and marginal probability density functions. The kernel function method is adopted to estimate the marginal probability density. The parameters of pair Copula functions are optimized by the maximum likelihood estimation. The developed method is analyzed and validated on the classification of neurophysiological signals. The obtained results show that it has better performance on several classification indexes.
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