Name
Abstract | ||
|---|---|---|
In some occasions, decision makers may have to deal with the problems in which only partial information is available. As a result, decision makers embody their preferences as incomplete fuzzy preference relations. In this paper, we propose an iterative procedure to estimate the missing values of the incomplete fuzzy preference relations that are assumed to be additive. The procedure is based on additive transitivity property. Measures of consistency and completeness of an incomplete fuzzy preference are also developed to assist decision makers to identify the quality of their decisions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74827-4_163 | KES (2) |
Keywords | Field | DocType |
incomplete fuzzy preference,missing values,partial information,missing value,incomplete fuzzy preference relation,iterative procedure,additive transitivity property,decision maker,incomplete additive fuzzy preference | Data mining,Computer science,Fuzzy logic,Artificial intelligence,Missing data,Completeness (statistics),Machine learning,Transitive relation | Conference |
Volume | ISSN | Citations |
4693 | 0302-9743 | 8 |
PageRank | References | Authors |
0.54 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsuan-shih Lee | 1 | 535 | 36.35 |
| Ming-Tao Chou | 2 | 76 | 7.15 |
| Hsin-Hsiung Fang | 3 | 12 | 1.62 |
| Wei-Kuo Tseng | 4 | 15 | 1.72 |
| Chen-Huei Yeh | 5 | 13 | 2.00 |