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dc.contributor.authorKo, Yu-Chienen_US
dc.contributor.authorFujita, Hamidoen_US
dc.contributor.authorTzeng, Gwo-Hshiungen_US
dc.date.accessioned2014-12-08T15:29:13Z-
dc.date.available2014-12-08T15:29:13Z-
dc.date.issued2013-01-01en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.knosys.2012.07.010en_US
dc.identifier.urihttp://hdl.handle.net/11536/21066-
dc.description.abstractThe fuzzy measure can highlight important information in analyzing component features, patterns, and trends. However, fuzzy densities and interaction effects are usually unknown or uncertain for implications thus making the fuzzy measure limited in applications. This research proposes an extended fuzzy measure to derive the conditional fuzzy densities from dominance-based rough set approach (DRSA), multiply preferences and the derived densities into utilities, fulfill fuzzy measure identification, and empower the fuzzy measure to aggregate utilities. For illustration, the extended fuzzy measure is applied on World Competitiveness Yearbook 2011 to imply policy-making information for Greece, Italy, Portugal, and Spain. (C) 2012 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectCompetitivenessen_US
dc.subjectDominance-based rough set approach (DRSA)en_US
dc.subjectFuzzy measureen_US
dc.subjectUtilityen_US
dc.subjectWorld Competitiveness Yearbook (WCY)en_US
dc.titleAn extended fuzzy measure on competitiveness correlation based on WCY 2011en_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.knosys.2012.07.010en_US
dc.identifier.journalKNOWLEDGE-BASED SYSTEMSen_US
dc.citation.volume37en_US
dc.citation.issueen_US
dc.citation.spage86en_US
dc.citation.epage93en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000313761800008-
dc.citation.woscount4-
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