INFERENCE AND DIAGNOSIS MODEL BASED ON BAYESIAN NETWORK AND ROUGH SETS THEORY

被引：0

作者：

Chang, Jui-Fang ^{[1
]}

Chen, Jung-Fang ^{[2
]}

Lee, Ming-Chang ^{[3
]}

机构：

[1] Natl Kaohsiung Univ Appl Sci, Dept Int Business, Kaohsiung, Taiwan

[2] Natl Kaohsiung Univ Appl Sci, Dept Business Adm, Kaohsiung, Taiwan

[3] Natl Kaohsiung Univ Appl Sci, Ctr Gen Educ, Kaohsiung, Taiwan

来源：

PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2 | 2014年

关键词：

Rough sets theory; Bayesian Networks; Decision rule;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rough set theory can be regarded as a new mathematical tool for imperfect data analysis. It is widely applied in knowledge reduction and rule extraction. Bayesian model is defined as the relationship between nodes and the node probability distribution. Therefore, this paper proposes a solution for reasoning and diagnosis model by using the rough sets and the Bayesian network to estimate the subjective of prior probability. The contribution of this paper is the combination of rough set theory and Bayesian Network to describe the change of analyzes influenza reasons. An example shows that the proposed method is correct and improves the capability of the reasoning and diagnosis model.

引用

页码：455 / 461

页数：7

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