Bayesian classifier based on D-vine Copula theory
Bayesian classifier based on D-vine Copula theory
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发表时间：
2019
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通讯作者：
Wang Bei;Sun Yu-dong;Jin Jing;Zhang Tao;Wang Xing-yu
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作者：
Wang Bei;Sun Yu-dong;Jin Jing;Zhang Tao;Wang Xing-yu
在传统的贝叶斯分类器中，如高斯判别分析方法和朴素贝叶斯方法，在构造变量的联合概率分布时，通常简化了变量之间的相关性。因此，对类条件概率密度的估计会与实际数据存在差异。本文通过研究变量间的相关性，提出了一种基于D-vine Copula理论的贝叶斯分类器。主要目的是提高类条件概率密度估计的准确性。将变量的联合概率分布分解为一系列对Copula函数和边际概率密度函数。采用核函数法估计边缘概率密度。利用极大似然估计对Copula函数的参数进行了优化。对该方法进行了神经生理信号的分类分析和验证。实验结果表明，该方法在多个分类指标上具有较好的性能。
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.