Name
Abstract | ||
|---|---|---|
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the database best. It turns out that when applied with a specific purpose in mind, such as classification, the performance of these network models may be very poor. We demonstrate that Bayesian network models should be created to meet the specific goal or purpose intended for the model. |
Year | Venue | Keywords |
|---|---|---|
1996 | ICML | network model,bayesian network,risk management,goal orientation,binary classification |
Field | DocType | Citations |
Variable-order Bayesian network,Pattern recognition,Goal orientation,Computer science,Risk management,Bayesian network,Artificial intelligence,Machine learning | Conference | 56 |
PageRank | References | Authors |
17.70 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuo J. Ezawa | 1 | 155 | 55.11 |
| Moninder Singh | 2 | 381 | 105.12 |
| Steven W. Norton | 3 | 181 | 62.53 |