Name
Abstract | ||
|---|---|---|
Rough set models based on the tolerance and similarity relations, are constructed to deal with incomplete information systems. Unfortunately, tolerance and similarity relations have their own limitations because the former is too loose while the latter is too strict in classification analysis. To make a reasonable and flexible classification in incomplete information system, a new binary relation is proposed in this paper. This new binary relation is only reflective and it is a generalization of tolerance and similarity relations. Furthermore, three different rough set models based on the above three different binary relations are compared and then some important properties are obtained. Finally, the direct approach to certain and possible rules induction in incomplete information system is investigated, an illustrative example is analyzed to substantiate the conceptual arguments. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/GRC.2009.5255034 | GrC |
Keywords | Field | DocType |
rough set theory,similarity relations,limited tolerance relation,binary relation,classification analysis,decision rules,knowledge acquisition,rough set model,tolerance relation,similarity relation,rough set,incomplete information system,computer science,information systems,decision rule,computational modeling,data mining,set theory,incomplete information | Incomplete information system,Information system,Data mining,Binary relation,Computer science,Artificial intelligence,Decision rule,Direct method,Set theory,Pattern recognition,Rough set,Machine learning,Knowledge acquisition | Conference |
ISBN | Citations | PageRank |
978-1-4244-4830-2 | 1 | 0.37 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi-bei Yang | 1 | 1211 | 66.36 |
| Jing-yu Yang | 2 | 6061 | 345.83 |
| Xiaohua Hu | 3 | 2819 | 314.15 |