On aggregating uncertain information by type-2 OWA operators for soft decision making

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

doi:10.1002/int.20420

Cited by 32 publications

(30 citation statements)

References 42 publications

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“…The majority of the contributions dealing with this fuzzy representation use interval type-2 fuzzy sets which maintain the uncertainty modelling properties of general type-2 fuzzy sets, but reducing the computational efforts that are needed to operate with them. Different aggregation operators for type-2 representation were introduced in 7,50 . As the type-1 linguistic based representation, the type-2 fuzzy sets computational based model needs to approximate the resulting type-2 fuzzy set from a linguistic operation by mapping the result into a linguistic assessment producing a loss of information.…”

Section: Classical Linguistic Computing Modelsmentioning

confidence: 99%

An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making

Rodríguez¹,

Labella²,

Martínez³

2016

IJCIS

192

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Decision makers involved in complex decision making problems usually provide information about their preferences by eliciting their knowledge with different assessments. Usually, the complexity of these decision problems implies uncertainty that in many occasions has been successfully modelled by means of linguistic information, mainly based on fuzzy based linguistic approaches. However, classically these approaches just allow the elicitation of simple assessments composed by either one label or a modifier with a label. Nevertheless, the necessity of more complex linguistic expressions for eliciting decision makers' knowledge has led to some extensions of classical approaches that allow the construction of expressions and elicitation of preferences in a closer way to human beings cognitive process. This paper provides an overview of the broadest fuzzy linguistic approaches for modelling complex linguistic preferences together some challenges that future proposals should achieve to improve complex linguistic modelling in decision making.

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Section: Classical Linguistic Computing Modelsmentioning

confidence: 99%

An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making

Rodríguez¹,

Labella²,

Martínez³

2016

IJCIS

192

View full text Add to dashboard Cite

show abstract

“…In another work, the authors in [16,15] presented the a-plane for T2 fuzzy sets (T2 FSs) which is useful for both theoretical and computational studies of these systems. Zhou et al [32] proposed a new operator for linguistic terms in human decision-making modeled by T2 0020-0255/$ -see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2010.…”

Section: Introductionmentioning

confidence: 99%

On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling

Biglarbegian

Melek

Mendel

2011

Information Sciences

108

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“…Note that it is commutative, monotonic, bounded and idempotent. For further reading on recent developments, refer, for example to Emrouznejad and Amin (2010), Merigó (2010), Merigó and Gil-Lafuente (2010), Yager (2010), Yager and Kacprzyk (1997), Zhou et al (2010).…”

Section: The Owa Operatormentioning

confidence: 98%

Induced and Linguistic Generalized Aggregation Operators and Their Application in Linguistic Group Decision Making

et al. 2010

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We introduce a wide range of induced and linguistic generalized aggregation operators. First, we present the induced linguistic generalized ordered weighted averaging (ILGOWA) operator. It is a generalization of the OWA operator that uses linguistic variables, order inducing variables and generalized means in order to provide a more general formulation. One of its main results is that it includes a wide range of linguistic aggregation operators such as the induced linguistic OWA (ILOWA), the induced linguistic OWG (ILOWG) and the linguistic generalized OWA (LGOWA) operator. We further generalize the ILGOWA operator by using quasi-arithmetic means obtaining the induced linguistic quasi-arithmetic OWA (Quasi-ILOWA) operator and by using hybrid averages forming the induced linguistic generalized hybrid average (ILGHA) operator. We also present a further extension with Choquet integrals. We call it the induced linguistic generalized Choquet integral aggregation (ILGCIA). We end the paper with an application of the new approach in a linguistic group decision making problem.

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On aggregating uncertain information by type-2 OWA operators for soft decision making

Cited by 32 publications

References 42 publications

An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making

An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making

On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling

Induced and Linguistic Generalized Aggregation Operators and Their Application in Linguistic Group Decision Making

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