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| Content Provider | Springer Nature Link |
|---|---|
| Author | Bernal, R. Karanik, M. Peláez, J. I. |
| Copyright Year | 2015 |
| Abstract | Modeling interactions between criteria in multiple criteria decision analysis (MCDA) is a complex task. Such complexity arises when there are visible redundancies and synergies among criteria, which traditional MCDA methods cannot deal with. The Choquet integral is a model that has been conceived to deal with these issues, but an appropriate fuzzy measure must be defined. This article shows how to compute a fuzzy measure for criteria coalitions using linguistic information efficiently. Due to the complexity to identify an adequate fuzzy measure when the criteria set cardinality increases, the proposed model reduces the effort to determine the measure of each criteria combination by focusing on relevant interactions. Then, this fuzzy measure is used on Choquet integral to establish the best alternative in a decision-making problem. Finally, a comparison between the arithmetic mean, the OWA operator and the proposed method is presented. |
| Starting Page | 1315 |
| Ending Page | 1327 |
| Page Count | 13 |
| File Format | |
| ISSN | 14327643 |
| Journal | Soft Computing |
| Volume Number | 20 |
| Issue Number | 4 |
| e-ISSN | 14337479 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2015-01-29 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Fuzzy measure Choquet integral Multiple criteria decision analysis Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Software Geometry and Topology |
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