Name
Abstract | ||
|---|---|---|
A Bayesian network is a powerful graphical model. It is advantageous for real-world data analysis and finding relations among variables. Knowledge presentation and rule generation, based on a Bayesian approach, have been studied and reported in many research papers across various fields. Since a Bayesian network has both causal and probabilistic semantics, it is regarded as an ideal representation to combine background knowledge and real data. Rare event predictions have been performed using several methods, but remain a challenge. We design and implement a Bayesian network model to forecast daily ozone states. We evaluate the proposed Bayesian network model, comparing it to traditional decision tree models, to examine its utility. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.knosys.2009.02.004 | Knowl.-Based Syst. |
Keywords | Field | DocType |
bayesian network,rare event prediction,bayesian network model,proposed bayesian network model,real-world data analysis,knowledge presentation,bayesian approach,traditional decision tree model,powerful graphical model,sensor data,ozone forecasting,graphical model,ozone,decision tree,data analysis | Data mining,Variable-order Bayesian network,Computer science,Bayesian network,Influence diagram,Bayesian programming,Artificial intelligence,Bayesian statistics,Graphical model,Machine learning,Dynamic Bayesian network,Bayesian probability | Journal |
Volume | Issue | ISSN |
22 | 5 | Knowledge-Based Systems |
Citations | PageRank | References |
13 | 0.69 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seong-Pyo Cheon | 1 | 42 | 5.65 |
| Sungshin Kim | 2 | 210 | 64.17 |
| So Young Lee | 3 | 36 | 4.97 |
| Chong-Bum Lee | 4 | 13 | 0.69 |