Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations

Ben‐Arieh, David; Chen, Zhifeng

doi:10.1109/tsmca.2005.853488

Cited by 239 publications

(126 citation statements)

References 37 publications

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“…Within this framework of preference representation, different consensus measurement based on similarity measures have been put forward by Herrera-Viedma, et al [37] and Wu and Chiclana [63] for both complete and incomplete information environments. The case when experts' opinions are expressed by means of linguistic assessments has been extensively studied and it is worth mentioning the works of Ben-Arieh and Chen [12], Cabrerizo, Alonso and Herrera-Viedma [14], García-Lapresta, Pérez-Román [30], Herrera, Herrera-Viedma and Verdegay [36], Herrera-Viedma, et al [40], Pérez-Asurmendi and Chiclana [53] and Wu, Chiclana and Herrera-Viedma [65]. Finally, models to reach consensus where experts assess their preferences using different preference representation structures (preference orderings, utility functions, multiplicative preference relations and fuzzy preference relations) have also been studied and proposed by Dong and Zhang [23], Fedrizzi et al [26] and Herrera-Viedma, Herrera and Chiclana [39].…”

Section: Consensus Measurement In the Literaturementioning

confidence: 99%

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A new measure of consensus with reciprocal preference relations: The correlation consensus degree

González-Arteaga

Calle

Chiclana

2016

Knowledge-Based Systems

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The achievement of a 'consensual' solution in a group decision making problem depends on experts' ideas, principles, knowledge, experience, etc. The measurement of consensus has been widely studied from the point of view of different research areas, and consequently different consensus measures have been formulated, although a common characteristic of most of them is that they are driven by the implementation of either distance or similarity functions. In the present work though, and within the framework of experts' opinions modelled via reciprocal preference relations, a different approach to the measurement of consensus based on the Pearson correlation coefficient is studied. The new correlation consensus degree measures the concordance between the intensities of preference for pairs of alternatives as expressed by the experts. Although a detailed study of the formal properties of the new correlation consensus degree shows that it verifies important properties that are common either to distance or to similarity functions between intensities of preferences, it is also proved that it is different to traditional consensus measures. In order to emphasise novelty, two applications of the proposed methodology are also included. The first one is used to illustrate the computation process and discussion of the results, while the second one covers a real life application that makes use of data from Clinical Decision-Making.

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Section: Consensus Measurement In the Literaturementioning

confidence: 99%

“…This fact could add to the consideration of the type of disorder as a factor to be taken into account in Clinical Decision-Making. 1.00 0.98 0.99 0.99 P (10) 1.00 0.98 0.97 P (11) 1.00 0.99 P (12) 1.00 Table 4: Correlation consensus degree (CCD) between pairs of patients.…”

Section: A Real Application: Concordance Among Patients' Preferencesmentioning

confidence: 99%

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

González-Arteaga

Calle

Chiclana

2016

Knowledge-Based Systems

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“…The MCDM methods have been widely used as useful tool due to its broad applications in real world problems 4,29,36,41 . When MCDM methods are extended for groups of decision makers, these methods are denominated multicriteria group decision-making methods (MCGDM) 2,5,17,20 .…”

Section: Introductionmentioning

confidence: 99%

Pure linguistic PROMETHEE I and II methods for heterogeneous MCGDM problems

Espinilla¹,

Halouani²,

Chabchoub³

2015

IJCIS

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The PROMETHEE methods basic principle is focused on a pairwise comparison of alternatives for each criterion, selecting a preference function type that often requires parameters in order to obtain a preference value. When a MCGDM problem is defined in an heterogeneous context, an adequate and common approach is to unify the involved information in linguistic values. However, for each criterion, there is a difficulty to select the specific preference function and define its parameters because they are expressed by crisp values in the unit interval, when the information involved in the problem has been unified into linguistic values. In this paper, a methodology for modeling linguistic preference functions in order to facilitate the selecting of each linguistic preference function type and the definition of its parameters is proposed, providing a more realistic definition of the criteria. Therefore, a generic linguistic preference function is proposed whose inputs and outputs are linguistic values. According to the generic linguistic preference function, six basic preference function types are extended for linguistic values. To do so, a linguistic difference function between linguistic values is defined, being its output, the input of the linguistic preference function. Furthermore, the proposed methodology is integrated in linguistic PROMETHEE I and II for heterogeneous MCGDM problems to obtain partial rankings and a full ranking of alternatives. So, the methodology provides pure linguistic PROMETHEE I and II that offer interpretability and understandability. Finally, the feasibility and applicability of pure linguistic PROMETHEE I and II are illustrated in a case study for the selection of a green supplier.

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“…To handle the uncertainty and linguistic information inherent to human consensus processes, many group decision-making techniques have been developed and are available in the academic literature [2,7,13,14,16,17]. In [18] a review of consensus models in a fuzzy environment can be found.…”

Section: Introductionmentioning

confidence: 99%

A consensus model for Delphi processes with linguistic terms and its application to chronic pain in neonates definition

Agell

Ganzewinkel

Sánchez

et al. 2015

Applied Soft Computing

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This paper proposes a new model of consensus based on linguistic terms to be implemented in Delphi processes. The model of consensus involves qualitative reasoning techniques and is based on the concept of entropy. The proposed model has the ability to reach consensus automatically without the need for either a moderator or a final interaction among panelists. In addition, it permits panelists to answer with different levels of precision depending on their knowledge on each question. The model defined has been used to establish the relevant features for the definition of a type of chronic disease. A real-case application conducted in the Department of Neonatology of Máxima Medical Center in The Netherlands is presented. This application considers the opinions of stakeholders of neonate healthcare in order to reach a final consensual definition of chronic pain in neonates.

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Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations

Cited by 239 publications

References 37 publications

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

Pure linguistic PROMETHEE I and II methods for heterogeneous MCGDM problems

A consensus model for Delphi processes with linguistic terms and its application to chronic pain in neonates definition

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