Name
Abstract | ||
|---|---|---|
We describe the results of performing data min- ing on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be difficult, yielding little more than 60% pre- dictive accuracy for most human and machine di- agnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classifier, is optimal. By comparing the performance of the naive Bayesian classifier to its more general cousin, the Bayesian network clas- sifter, and to selective Bayesian classifiers with just 10% of the total attributes, we show that the simplest models perform at least as well as the more complex models. We argue that simple models like the selective naive Bayesian classifier will perform as well as more complicated mod- els for similarly complex domains with relatively small data sets, thereby calling into question the extra expense necessary to induce more complex models. |
Year | Venue | Keywords |
|---|---|---|
1996 | KDD | bayesian classifier,bayesian network,medical diagnosis,data mining |
Field | DocType | Citations |
Data mining,Variable-order Bayesian network,Small data,Computer science,Bayesian network classifier,Artificial intelligence,Medical diagnosis,Machine learning,Bayesian probability,Naive bayesian classifier | Conference | 4 |
PageRank | References | Authors |
0.57 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| gregory provan | 1 | 503 | 120.02 |
| Moninder Singh | 2 | 381 | 105.12 |