Name
Abstract | ||
|---|---|---|
In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realistic medical domain--stroke. Basically, the domain of stroke is taken as a typical example of a medical domain where much data are available concerning a few hundred patients. Learning the structure of a Bayesian network is known to be hard under these conditions. In this paper, two different structure learning algorithms are compared to each other. A causal model which was constructed with the help of an expert clinician is adopted as the gold standard. The advantages and limitations of various structure-learning algorithms are discussed in the context of the experimental results obtained. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45497-7_46 | ISMDA |
Keywords | Field | DocType |
realistic medical domains,bayesian network,gold standard,realistic medical domain,different structure,expert clinician,causal model,learning bayesian-network topologies,medical domain,recent year,hundred patient,knowledge discovery,machine learning | Variable-order Bayesian network,Instance-based learning,Active learning (machine learning),Computer science,Network topology,Bayesian network,Artificial intelligence,Knowledge extraction,Computational learning theory,Machine learning,Causal model | Conference |
ISBN | Citations | PageRank |
3-540-42734-1 | 4 | 0.46 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofeng Wu | 1 | 11 | 2.04 |
| Peter J. F. Lucas | 2 | 637 | 65.68 |
| Susan Kerr | 3 | 4 | 0.46 |
| Roelf Dijkhuizen | 4 | 4 | 0.46 |