Name
Abstract | ||
|---|---|---|
In this paper, we propose a new approach of classification based on rough sets denoted Dynamic Belief Rough Set Classifier (D-BRSC) which is able to learn decision rules from uncertain data. The uncertainty appears only in decision attributes and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. The feature selection step of the construction procedure of our new technique of classification is based on the calculation of dynamic reduct. The reduction of uncertain and noisy decision table using dynamic approach which extracts more relevant and stable features yields more significant decision rules for the classification of the unseen objects. To prove that, we carry experimentations on real databases using the classification accuracy criterion. We also compare the results of D-BRSC with those obtained from Static Belief Rough Set Classifier (S-BRSC). |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13529-3_39 | RSCTC |
Keywords | Field | DocType |
dynamic reduct,dynamic approach,transferable belief model,decision rule,decision attribute,classification accuracy criterion,dynamic belief rough set,rough set classifier,noisy decision table,static belief rough set,significant decision rule,static belief,classification,rough set,uncertainty,feature selection,rough sets,decision table | Decision rule,Reduct,Decision table,Feature selection,Pattern recognition,Uncertain data,Rough set,Artificial intelligence,Transferable belief model,Machine learning,Dominance-based rough set approach,Mathematics | Conference |
Volume | ISSN | ISBN |
6086 | 0302-9743 | 3-642-13528-5 |
Citations | PageRank | References |
2 | 0.38 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salsabil Trabelsi | 1 | 41 | 4.84 |
| Zied Elouedi | 2 | 694 | 77.53 |
| Pawan Lingras | 3 | 1408 | 143.21 |