Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMLC.2014.7009651 | ICMLC |
Keywords | Field | DocType |
rough set theory,belief networks,bayesian networks,inference mechanisms,diagnosis model,data analysis,decision rule,bayesian model,knowledge reduction,knowledge acquisition,inference model,imperfect data analysis,rule extraction,rough sets theory,node probability distribution,bayesian network,probability,probabilistic logic | Data mining,Bayesian inference,Computer science,Artificial intelligence,Bayesian statistics,Dominance-based rough set approach,Variable-order Bayesian network,Pattern recognition,Empirical probability,Rough set,Bayesian network,Dempster–Shafer theory,Machine learning | Conference |
Volume | ISSN | Citations |
2 | 2160-133X | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jui-Fang Chang | 1 | 0 | 1.69 |
| Jung-Fang Cheng | 2 | 0 | 0.34 |
| Ming-Chang Lee | 3 | 82 | 15.03 |