Type-1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology. Application to Eurobonds Credit Risk Evaluation
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Wiley Online Library
T1OWA operatorLinguistic hierarchyUnbalanced fuzzy linguistic assessmentsCredit quality
Chiclana Parrilla, Francisco; et. al. Type-1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology. Application to Eurobonds Credit Risk Evaluation. International Journal of Intelligent Systems, Vol. 33, 1071–1088 (2018) [http://hdl.handle.net/10481/51213]
SponsorshipThis research work has been supported by the research projects grants (TIN2013-40658-P and TIN2016- 75850-R) from the FEDER funds, and the University of Granada `Strengthening through Short-Visits' (Ref. GENIL-SSV 2015) programme.
In decision making, a widely used methodology to manage unbalanced fuzzy linguistic informa- tion is the linguistic hierarchy (LH), which relies on a linguistic symbolic computational model based on ordinal 2-tuple linguistic representation. However, the ordinal 2-tuple linguistic approach does not exploit all advantages of Zadeh’s fuzzy linguistic approach to model uncertainty because the membership function shapes are ignored. Furthermore, the LH methodology is an indirect approach that relies on the uniform distribution of symmetric linguistic assessments. These draw- backs are overcome by applying a fuzzy methodology based on the implementation of the type-1 ordered weighted average (T1OWA) operator. The T1OWA operator is not a symbolic opera- tor and it allows to directly aggregate membership functions, which in practice means that the T1OWA methodology is suitable for both balanced and unbalanced linguistic contexts and with heterogeneous membership functions. Furthermore, the final output of the T1OWA methodology is always fuzzy and defined in the same domain of the original unbalanced fuzzy linguistic labels, which facilitates its interpretation via a visual joint representation. A case study is presented where the T1OWA operator methodology is used to assess the creditworthiness of European bonds based on real credit risk ratings of individual Eurozone member states modeled as unbalanced fuzzy linguistic labels.