The problem of linguistic approximation in clinical decision making

Degani, R.; Bortolan, G.

doi:10.1016/0888-613x(88)90073-4

Cited by 55 publications

(63 citation statements)

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“…This computational model makes use of the Extension Principle from fuzzy arithmetic (Degani and Bortolan 1988): the result of an aggregation functionF over a set of n linguistic labels in the term set T (L) is a fuzzy number F(R) that usually does not have an associated linguistic label on T (L). Therefore, it is necessary to apply an approximation function app 1 (·) to associate it to a particular label on T (L)-the retranslation problem (Martin and Klir 2006;Yager 2004)-or to use fuzzy ranking procedures to obtain a final order of the alternatives: …”

Section: Linguistic Computational Model Based On Membership Functionsmentioning

confidence: 99%

Computing with words in decision making: foundations, trends and prospects

Herrera

Alonso

Chiclana

et al. 2009

Fuzzy Optim Decis Making

456

187

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Computing with Words (CW) methodology has been used in several different environments to narrow the differences between human reasoning and computing. As Decision Making is a typical human mental process, it seems natural to apply the CW methodology in order to create and enrich decision models in which the information that is provided and manipulated has a qualitative nature. In this paper we make a review of the developments of CW in decision making. We begin with an overview of the CW methodology and we explore different linguistic computational models that have been applied to the decision making field. Then we present an historical perspective of CW in decision making by examining the pioneer papers in the field along with its most recent applications. Finally, some current trends, open questions and prospects in the topic are pointed out.

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Section: Linguistic Computational Model Based On Membership Functionsmentioning

confidence: 99%

Computing with words in decision making: foundations, trends and prospects

Herrera

Alonso

Chiclana

et al. 2009

Fuzzy Optim Decis Making

456

187

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“…The approximative computational model based on the Extension Principle [2,5]. This model uses fuzzy arithmetic based on the Extension Principle to make computations over the linguistic variables.…”

Section: Fuzzy Linguistic Approachmentioning

confidence: 99%

A communication model based on the 2-tuple fuzzy linguistic representation for a distributed intelligent agent system on Internet

Delgado

Herrera

Herrera‐Viedma

et al. 2002

Soft Computing - A Fusion of Foundations, Methodologies and App

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Internet users are assisted by means of distributed intelligent agents in the information gathering process to find the fittest information to their needs. In this paper we present a distributed intelligent agent model where the communication of the evaluation of the retrieved information among the agents is carried out by using linguistic operators based on the 2-tuple fuzzy linguistic representation as a way to endow the retrieval process with a higher flexibility, uniformity and precision. The 2-tuple fuzzy linguistic representation model allows to make processes of computing with words without loss of information. IntroductionIn the framework of the information retrieval one of the most current problems nowadays for which the fuzzy linguistic approach may be very useful, is the retrieval, handling and identification of relevant information through the Internet.The fuzzy linguistic approach is an approximate technique, which represents qualitative aspects as linguistic values by means of linguistic variables, that is, variables whose values are not numbers but words or sentences in a natural or artificial language [36]. This approach has been applied successfully to different areas as economics [14,31], planning [1], decision-making [12, 32], information retrieval [3,9,18,19], etc.Intelligent agents [4,11,26,29,34] deal with the information gathering process assisting the Internet users to find the fittest information to their needs. Several proposals about intelligent software agents have been emerging in the recent last years, but the lack of connection, communication and consensus among them have lead to a decrease in the quality and suitability of the retrieved information besides the efficiency of the system in the recovering and filtering task. This fact keeps the need of proposals in the field, and emphasizes the importance of the design and development of intelligent software agent organisations, as well as hierarchies and architectures that hold up such structures [7,10,21,28,29].However, not only is needed some organization, but also a protocol of communication among the agents. The great variety of representations and evaluations of the information in the Internet is the main obstacle to this communication, and the problem becomes more noticeable when the user takes part in the process. The complexity of all these processes reveals the need of more flexibility in the communication among agents and between agents and the user [9,34,35]. For this purpose, several approaches related to mechanisms to introduce and handle flexible information through linguistic labels have been proposed both at levels of agents and users [8,33].The main drawback of these approaches is the lack of precision in the final results, due to the fact that appears a loss of information in the processes of computing with words (CW). To overcome this drawback in [15] was presented a linguistic computational model based on linguistic 2-tuples which provides a computational technique to deal with linguistic information in a precise ...

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“…Linguistic preference information plays an important role in decision making (Zadeh 1983;Yager 1984Yager , 2004Kacprzyk 1986;Degani and Bortolan 1988;Delgado et al 1992;Liu et al 1994;Bordogna et al 1997;Zadeh and Kacprzyk 1999;Carlsson and Fullér 2000a,b;Herrera and Martínez 2001;Cordon et al 2002;Wang and Chuu 2004;Xu 2004a;Xu and Da 2003, etc.). Recently, some researchers have given more attention to group decision making under linguistic preference relations.…”

Section: Introductionmentioning

confidence: 97%

A Practical Procedure for Group Decision Making under Incomplete Multiplicative Linguistic Preference Relations

Zhang

2006

Group Decis Negot

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The aim of this paper is to investigate a group decision making problem with incomplete multiplicative linguistic preference relations. We first define the concept of an incomplete multiplicative linguistic preference relation, and then develop a simple algorithm to extend each incomplete multiplicative linguistic preference relation to a complete multiplicative linguistic preference relation. Finally, we develop a practical procedure for group decision making under incomplete multiplicative linguistic preference relations, and give a numerical example to illustrate the developed procedure.

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The problem of linguistic approximation in clinical decision making

Cited by 55 publications

References 0 publications

Computing with words in decision making: foundations, trends and prospects

Computing with words in decision making: foundations, trends and prospects

A communication model based on the 2-tuple fuzzy linguistic representation for a distributed intelligent agent system on Internet

A Practical Procedure for Group Decision Making under Incomplete Multiplicative Linguistic Preference Relations

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