Name
Abstract | ||
|---|---|---|
Investigating clusters of experts is an interesting topic in the large-group decision-making (LGDM) problem, since being familiar with patterns (groups) of experts is beneficial to some other actions needed for decision-making (e.g., reconciliation of opinions derived from different expert groups). However, not too much attention has been paid to expert clustering in the LGDM problem under a linguistic environment. Besides, it seems that only the decision information is utilized to group experts while the auxiliary (outside) knowledge (e.g., expertise and occupation) about these experts has not been fully considered during the clustering process. To address this issue, this study proposes a hybrid method integrating outside knowledge about experts with practical preference information under the interval-valued linguistic environment to cluster experts. The method consists of four elements: pre-clustering of experts according to the given knowledge, the optimization model to transform the interval-valued 2-tuple linguistic (IV2TL) decision information, the data envelopment analysis-discriminant analysis (DEA-DA) model to deal with a two-cluster issue, and iterative clustering based on the DEA-DA model to cluster experts into multiple clusters. The feasibility and validity of the proposed method are illustrated with a real-world example. A comparison with the maximal tree clustering method in the linguistic environment is provided. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/JIFS-191092 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Large-group decision-making (LGDM),interval-valued 2-tuple linguistic (IV2TL) representation model,outside knowledge,expert clustering | Natural language processing,Artificial intelligence,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
37 | 5.0 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingsheng Liu | 1 | 177 | 8.56 |
| Yuan Chen | 2 | 1 | 1.02 |
| Yinghua Shen | 3 | 6 | 1.47 |
| Xianfei Yin | 4 | 1 | 0.77 |