Name
Abstract | ||
|---|---|---|
Knowledge reduction is one of the basic contents in rough set theory and one of the most problem in knowledge acquisition. The main objective of this paper is to introduce a new concept of knowledge reduction based on partition. It is referred to as partition reduction. The partition reduction is to unify the definitions of classical knowledge reductions. Classical knowledge reductions such as absolute attribute reduction, relative reduction, distribution reduction, assignment reduction and maximum distribution reduction are special cases of partition reduction. We can establish new types of knowledge reduction to meet our requirements based on partition reduction. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11508069_7 | IDEAL |
Keywords | Field | DocType |
maximum distribution reduction,distribution reduction,absolute attribute reduction,classical knowledge reduction,rough set,partition reduction,knowledge acquisition,knowledge reduction,assignment reduction,new concept,relative reduction,rough set theory | Descriptive knowledge,Computer science,Rough set,Artificial intelligence,Knowledge base,Partition (number theory),Machine learning,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
3578 | 0302-9743 | 3-540-26972-X |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaobing Pei | 1 | 26 | 7.78 |
| Yuanzhen Wang | 2 | 86 | 11.78 |