Name
Abstract | ||
|---|---|---|
In this paper, we deal with the problem of rule discovery process based on rough sets from partially uncertain data. The uncertainty exists only in decision attribute values and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. To solve this problem, we propose in this uncertain environment, a new method based on a soft hybrid induction system for discovering classification rules called GDT-RS which is a hybridization of the Generalization Distribution Table and the Rough Set methodology. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14049-5_74 | IPMU |
Keywords | Field | DocType |
transferable belief model,rule discovery process,uncertain data,rough set,belief function framework,rough set methodology,generalization distribution,new method,decision attribute value,uncertain environment,classification rule,belief function theory | Computer science,Uncertain data,Rough set,Belief function theory,Artificial intelligence,Transferable belief model,Business process discovery,Machine learning,Dominance-based rough set approach | Conference |
ISBN | Citations | PageRank |
3-642-14048-3 | 2 | 0.37 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salsabil Trabelsi | 1 | 41 | 4.84 |
| Zied Elouedi | 2 | 694 | 77.53 |
| Pawan Lingras | 3 | 1408 | 143.21 |