Name
Abstract | ||
|---|---|---|
Bayesian multinets are a Bayesian networks extension where context-specific conditional independences can be represented. The main aim of this work is to study different methods to choose the distinguished attribute in Bayesian multinets when we use them in supervised classification tasks. We have used different approaches: a wrapper method and several filter methods. This will allow us to determine the most appropriate approach that meets our requirements of accuracy and/or time. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11518655_78 | ECSQARU |
Keywords | Field | DocType |
bayesian multinets classifier,context-specific conditional independence,main aim,filter method,appropriate approach,distinguished attribute,supervised classification task,bayesian multinets,different method,different approach,bayesian networks extension,bayesian network,conditional independence | Data mining,Conditional independence,Computer science,Bayesian network,Information extraction,Artificial intelligence,Information gain ratio,Machine learning,Branching (version control),Bayesian probability | Conference |
Volume | ISSN | ISBN |
3571 | 0302-9743 | 3-540-27326-3 |
Citations | PageRank | References |
5 | 0.41 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Cano | 1 | 193 | 20.06 |
| Francisco Javier García Castellano | 2 | 72 | 4.84 |
| Andrés R. Masegosa | 3 | 256 | 26.13 |
| S. Moral | 4 | 5 | 0.41 |