Name
Abstract | ||
|---|---|---|
The Dempster-Shafer theory (DST) is particularly interesting to deal with imprecise information. However, it is known for its high computational cost, as dealing with a frame of discernment Ω involves the manipulation of up to 2|Ω| elements. Hence, classification problems where the number of classes is too large cannot be considered. In this paper, we propose to take advantage of a context of ensemble classification to construct a frame of discernment where only a subset of classes is considered. We apply this method to script recognition problems, which by nature involve a tremendous number of classes. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22152-1_24 | ECSQARU |
Keywords | Field | DocType |
tremendous number,imprecise information,high computational cost,large number,ensemble classification,classification problem,dempster-shafer theory,script recognition problem,dynamic frame,dempster shafer theory,data fusion | Computer science,Sensor fusion,Artificial intelligence,Dempster–Shafer theory,Script recognition,Machine learning,Discernment | Conference |
Citations | PageRank | References |
3 | 0.40 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yousri Kessentini | 1 | 100 | 15.39 |
| Thomas Burger | 2 | 38 | 5.81 |
| Thierry Paquet | 3 | 565 | 56.65 |