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Research and Application of Bayesian Classification
DOI: 10.12677/PM.2023.1310312, PDF, HTML, XML, 下载: 165  浏览: 267

Abstract: This paper studies the research status of Bayesian classification and its application in practice. In the first half of this paper, the research status of Bayesian classification is introduced. In the last part of the paper, the principal component analysis method is introduced and realized, and the results of principal component analysis are visualized by using the rubble diagram. Then, an im-proved weighted Bayesian classification method based on principal component analysis is proposed by combining the processed data with weighted attributes and Bayesian classifier. Secondly, the probability of feature and category of feature is calculated by mutual information, which is used as the prior probability of Bayesian classifier for classification, and a Bayesian classification algorithm for feature selection of mutual information is presented. Finally, numerical experiments show that the proposed two improved methods have good classification performance.

1. 引言

2. 贝叶斯分类的研究现状

3. 基于主成分分析的加权贝叶斯分类

3.1. 主成分分析的过程

Figure 1. Principal component lithotripsy diagram

Figure 2. Scores of each variable

Figure 3. Correlation coefficient

3.2. 加权属性的贝叶斯分类

Table 1. Comparison table of weighted naive bayes classifier results

4. 基于互信息特征选择的贝叶斯分类

4.1. 互信息的基本原理

$I\left(X;Y\right)=\underset{y\in Y}{\sum }\underset{x\in X}{\sum }p\left(x,y\right)\mathrm{log}\left(\frac{p\left(x,y\right)}{p\left(x\right)p\left(y\right)}\right)$ (1)

$I\left(X;Y\right)={\int }_{Y}{\int }_{X}p\left(x,y\right)\mathrm{log}\left(\frac{p\left(x,y\right)}{p\left(x\right)p\left(y\right)}\right)\text{d}x\text{d}y$ (2)

4.2. 改进算法应用

$MI\left(t,{c}_{i}\right)=\mathrm{log}\frac{P\left(t|{c}_{i}\right)}{P\left(t\right)×P\left({c}_{i}\right)}=\mathrm{log}\frac{P\left(t|{c}_{i}\right)}{P\left(t\right)}$ (3)

Table 2. Statistical table of characteristic words

5. 总结

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