Name
Abstract | ||
|---|---|---|
Rough set theory is a new soft computing tool to deal with vagueness and uncertainty. It has attracted much attention of many researchers and practitioners all over the world, and has been applied to many fields successfully such as knowledge discovery, decision support, pattern recognition, machine learning, etc. Though the rough set theory is founded upon the solid mathematics base, there are still many theoretical problems to be solved. In this paper, the relationship between the rough set theory and the DS evidence theory and the relationship between the rough set theory and the fuzzy set theory are discussed, the extension of the rough set theory and the rough set theory based reasoning (abbr. rough reasoning) mechanism are emphasized, and a new effective algorithm for finding all the absolute reductions in a given information system is presented. Moreover, a new algorithm of attribute values reduction and rule generation is also proposed. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICSMC.2004.1401135 | 2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7 |
Keywords | Field | DocType |
rough sets, DS evidence theory, fuzzy set theory, rough reasoning, attributes reduction | Vagueness,Computer science,Proof theory,Fuzzy set,Rough set,Knowledge extraction,Artificial intelligence,Soft computing,Machine learning,Complete information,Dominance-based rough set approach | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunliang Jiang | 1 | 134 | 22.20 |
| Congfu Xu | 2 | 131 | 15.71 |
| Jin Gou | 3 | 34 | 4.09 |
| Zuxin Li | 4 | 0 | 0.34 |