Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers

Shang Ming Zhou, Francisco Chiclana, Robert I. John, Jonathan M. Garibaldi

Research output: Contribution to journalArticle

128 Citations (Scopus)

Abstract

The OWA operator proposed by Yager has been widely used to aggregate experts' opinions or preferences in human decision making. Yager's traditional OWA operator focuses exclusively on the aggregation of crisp numbers. However, experts usually tend to express their opinions or preferences in a very natural way via linguistic terms. These linguistic terms can be modelled or expressed by (type-1) fuzzy sets. In this paper, we define a new type of OWA operator, the type-1 OWA operator that works as an uncertain OWA operator to aggregate type-1 fuzzy sets with type-1 fuzzy weights, which can be used to aggregate the linguistic opinions or preferences in human decision making with linguistic weights. The procedure for performing type-1 OWA operations is analysed. In order to identify the linguistic weights associated to the type-1 OWA operator, type-2 linguistic quantifiers are proposed. The problem of how to derive linguistic weights used in type-1 OWA aggregation given such type of quantifier is solved. Examples are provided to illustrate the proposed concepts. Crown

Original languageEnglish
Pages (from-to)3281-3296
Number of pages16
JournalFuzzy Sets and Systems
Volume159
Issue number24
Early online date5 Jul 2008
DOIs
Publication statusPublished - 16 Dec 2008

Keywords

  • aggregation
  • OWA operator
  • soft decision making
  • type-1 OWA operator
  • type-2 fuzzy sets
  • type-2 linguistic quantifiers

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