Name
Abstract | ||
|---|---|---|
A Bayesian Network (BN) consists of a qualitative part representing the structural assumptions of the domain and a quantitative part, the parameters. To date, knowledge engineering support has focused on parameter elicitation, with little support for designing the graphical structure. Poor design choices in BN construction can impact the network's performance, network maintenance, and the explanatory power of the output. We present a tool to help domain experts examine BN structure independently of the parameters. Our qualitative evaluation of the tool shows that it can help in identifying possible structural modeling errors and, hence, improve the quality of BN models. (c) 2006 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1002/int.20175 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
bayesian network | Data mining,Computer science,Explanatory power,Bayesian network,Visual tool,Artificial intelligence,Knowledge engineering,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 11 | 0884-8173 |
Citations | PageRank | References |
2 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tal Boneh | 1 | 14 | 2.67 |
| Ann E. Nicholson | 2 | 692 | 88.01 |
| E. A. Sonenberg | 3 | 100 | 37.96 |