Name
Abstract | ||
|---|---|---|
This paper applies a Bayesian network to model multi criteria distribution maps and to discover knowledge contained in spatial data. The procedure consists of three steps: pre processing map data, training the Bayesian Network model using distribution maps of Australia and testing the generalization and diagnosis of the model using individual states' maps. The Bayesian network that we used in this study is known as naïve Bayesian network. Results show that this environmental Bayesian network model can generalize the classification rules from training data for good prediction and diagnosis of a distribution map. |
Year | Venue | Keywords |
|---|---|---|
2006 | AusDM | training data,pre processing map data,bayesian network,spatial data,multi criteria distribution map,environmental bayesian network model,bayesian network model,good prediction,distribution map,classification rule |
Field | DocType | ISBN |
Data mining,Variable-order Bayesian network,Naive Bayes classifier,Computer science,Bayesian network,Bayesian programming,Artificial intelligence,Bayesian hierarchical modeling,Bayesian statistics,Graphical model,Machine learning,Dynamic Bayesian network | Conference | 1-920682-41-4 |
Citations | PageRank | References |
2 | 0.41 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Norazwin Buang | 1 | 2 | 0.41 |
| Nianjun Liu | 2 | 162 | 15.01 |
| Terry Caelli | 3 | 1175 | 151.70 |
| Rob Lesslie | 4 | 4 | 0.83 |
| Michael J. Hill | 5 | 42 | 12.10 |