Name
Title | ||
|---|---|---|
Optimal granulation selection for similarity measure-based multigranulation intuitionistic fuzzy decision-theoretic rough sets. |
Abstract | ||
|---|---|---|
Similarity measure is an important uncertainty measurement in intuitionistic fuzzy set (IFS) theory. In this study, a novel similarity measure is presented by the combination of the information carried by hesitancy degree and the endpoint distance of membership and nonmembership, respectively. Moreover, a numerical example is used to verify the reasonable of the proposed similarity measure. After that, the similarity measure is applied to construct the IF decision-theoretic rough set (IF-DTRS) model and multigranulation IF decision-theoretic rough set (MG-IF-DTRS) model. Some properties of IF-DTRS and MG-IF-DTRS are also investigated. Thirdly, based on granular significance, a novel approach of optimal granulation selection is formulated. Finally, a heuristic algorithm is designed and the effectiveness of this algorithm is demonstrated by an illustrative example. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/JIFS-181193 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Similarity measure,Decision-theoretic rough set,Intuitionistic fuzzy sets,Rough set,Multigranulation rough set,Granulation selection | Similarity measure,Rough set,Artificial intelligence,Granulation,Mathematics,Fuzzy decision,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 3 | 1064-1246 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meishe Liang | 1 | 2 | 2.40 |
| Ju-Sheng Mi | 2 | 2054 | 77.81 |
| Tao Feng | 3 | 282 | 33.77 |