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computing with words: Revisiting the Qualitative Scale

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2018年第SUPPL.+2期26卷 127-143页

作者： Zuheros, Cristina Li, Cong-Cong Javier Cabrerizo, Francisco Dong, Yucheng Herrera-Viedma, Enrique Herrera, Francisco Univ Granada Andalusian Res Inst Data Sci & Computat Intellige E-18071 Granada Spain Sichuan Univ Business Sch Chengdu Sichuan Peoples R China

computing with words (CW) is a methodology which takes an essential role in decision making problems, thus the first aim of this paper is to highlight the importance of CW in decision making. There are many proposals of CW models that can be classified in two main fields: approaches based on membership functions and approaches based on qualitative scales. This paper focuses on the qualitative scales since it provides a closer environment to human knowledge and more accurate results avoiding a loss of information. An analysis and discussion of the qualitative scales of linguistic values in CW for decision making is provided. Finally, we highlight the open challenges for the qualitative scales in linguistic decision making.

关键词： computing with words decision making semantic models qualitative scales 2-tuple linguistic model

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computing with words in intelligent database querying: standalone and Internet-based applications

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INFORMATION SCIENCES 2001年第1-4期134卷 71-109页

作者： Kacprzyk, J Zadrozny, S Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

We present how computing with words, meant as a set of fuzzy-logic-based tools for an effective and efficient handling of imprecise elements of natural language, can be implemented for fuzzy querying via a user-friendly interface to Microsoft Access, FQUERY for Access. The system accommodates fuzzy (imprecise) terms and linguistic quantifiers allowing for queries exemplified by "find tall) records such that most of the (important) clauses are satisfied (to a degree from [0, 1])". L.A. Zadeh's [Comput. Math. Appl. 9 (1983) 149] fuzzy logic based calculus of linguistically quantified propositions, and R.R. Yager's [IEEE Trans. Syst. Man Cybernet. 18 (1988) 183] ordered weighted averaging (OWA) operators are employed to deal with fuzzy linguistic quantifiers. It is then shown how FQUERY for Access, which is a standalone application, may be extended to support fuzzy querying via the Internet (or, analogously, Intranet). It is shown how WWW browsers, both the Netscape Navigator and Microsoft Explorer, can be employed for developing a fuzzy querying interface for handling imprecise natural language elements in database queries following Zadeh's computing with words paradigm. (C) 2001 Published by Elsevier Science Inc.

关键词： database querying flexible querying fuzzy querying fuzzy logic microsoft access fuzzy querying via Internet computing with words

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computing with words in Risk Assessment

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2010年第4期3卷 396-419页

作者： Liu, Jun Martinez, Luis Wang, Hui Rodriguez, Rosa Ma Novozhilov, Vasily Univ Ulster Sch Comp & Math Newtownabbey BT37 0QB North Ireland Univ Ulster Sch Built Environm Newtownabbey BT37 0QB North Ireland Univ Jaen Dept Comp Sci E-23071 Jaen Spain

This paper presents a comprehensive overview of currently known applications of computing with words (CWW) in risk assessment. It is largely grouped into the following 5 categories: (1) fuzzy number based risk assessment;(2) fuzzy rule-based risk assessment;(3) fuzzy extension of typical probabilistic risk assessment;(4) ordinal linguistic approach for risk assessment;and (5) miscellaneous applications. In addition, the role of CWW within the broad area of risk assessment is briefly characterized.

关键词： risk assessment linguistic information fuzzy logic computing with words

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computing with words WITH THE USE OF INVERSE RDM MODELS OF MEMBERSHIP FUNCTIONS

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2015年第3期25卷 675-688页

作者： Piegat, Andrzej Plucinski, Marcin West Pomeranian Univ Technol Fac Comp Sci & Informat Technol PL-71210 Szczecin Poland

computing with words is a way to artificial, human-like thinking. The paper shows some new possibilities of solving difficult problems of computing with words which are offered by relative-distance-measure RDM models of fuzzy membership functions. Such models are based on RDM interval arithmetic. The way of calculation with words was shown using a specific problem of flight delay formulated by Lotfi Zadeh. The problem seems easy at first sight, but according to the authors' knowledge it has not been solved yet. Results produced with the achieved solution were tested. The investigations also showed that computing with words sometimes offers possibilities of achieving better problem solutions than with the human mind.

关键词： computing with words fuzzy arithmetic RDM fuzzy arithmetic granular computing

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computing with words and its relationships with fuzzistics

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INFORMATION SCIENCES 2007年第4期177卷 988-1006页

作者： Mendel, Jerry M. Univ So Calif Inst Signal & Image Proc Dept Elect Engn Los Angeles CA 90089 USA

words mean different things to different people, and so are uncertain. We, therefore, need a fuzzy set model for a word that has the potential to capture their uncertainties. In this paper I propose that an interval type-2 fuzzy set (IT2 FS) be used as a FS model of a word, because it is characterized by its footprint of uncertainty (FOU), and therefore has the potential to capture word uncertainties. Two approaches are presented for collecting data about a word from a group of subjects and then mapping that data into a FOU for that word. The person MF approach, in which each person provides their FOU for a word, is limited to fuzzy set experts because it requires the subject to be knowledgeable about fuzzy sets. The interval end-points approach, in which each person provides the end-points for an interval that they associate with a word on a prescribed scale is not limited to fuzzy set experts. Both approaches map data collected from subjects into a parsimonious parametric model of a FOU, and illustrate the combining of fuzzy sets and statistics-type-2 fuzzistics. (C) 2006 Elsevier Inc. All rights reserved.

关键词： computing with words fuzzistics interval type-2 fuzzy sets type-2 fuzzy sets inverse problems

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computing with words for solving the fuzzy transportation problem

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SOFT computing 2023年 1-14页

作者： Pratihar, Jayanta Dey, Arindam Khan, Abhinandan Banerjee, Pritha Pal, Rajat Kumar Budge Budge Inst Technol Dept Comp Sci & Engn Kolkata 700137 West Bengal India Univ Calcutta Dept Comp Sci & Engn Acharya Prafulla Chandra Roy Siksha Prangan JD-2Sect 3 Kolkata 700106 West Bengal India VIT AP Univ Sch Comp Sci & Engn Amaravati 522237 Andhra Pradesh India ARP Engn Prod Dev & Diversificat 147 Nilgunj Rd Kolkata 700056 West Bengal India

computing with words is a soft computing technique in which computational objects are natural words and propositions are taken from some natural language. It offers a significant ability of computing human knowledge/information present in natural language. The main idea of computing with words is based on the remarkable ability of human beings to deal with several types of real-world decision-making problems without any measurements or computations. One of the most significant and fundamental fuzzy optimisation problems that appear as a sub-problem in several real-world applications is the fuzzy environment-oriented transportation problem, sometimes known as the fuzzy transportation problem. In fuzzy transportation problems, we use type-1 fuzzy set or fuzzy numbers to represent the transportation cost, supply or demand. However, humans describe these variables in their daily life using terms like large, medium, small, some, etc., which do not supply any real or fuzzy number. In this article, the motivation is to find an algorithm for transportation problems that is simple enough and effective in real-world scenarios. Hence, we propose an algorithm to solve the transportation problem based on computing with words, where some realistic words (e.g. large, medium, small, or tiny) are considered for representing transportation cost, supply, and demand. The efficiency of our suggested approach is demonstrated with a few numerical examples.

关键词： Fuzzy transportation problem Natural language Interval type-2 fuzzy set computing with words

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

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FUZZY OPTIMIZATION AND DECISION MAKING 2009年第4期8卷 337-364页

作者： Herrera, F. Alonso, S. Chiclana, F. Herrera-Viedma, E. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Granada Software Engn Dept Granada Spain De Montfort Univ Ctr Computat Intelligence Leicester LE1 9BH Leics England

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.

关键词： computing with words Decision making Linguistic information

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computing with words for student strategy evaluation in an examination

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GRANULAR computing 2019年第2期4卷 167-184页

作者： Gupta, Prashant K. Muhuri, Pranab K. South Asian Univ Dept Comp Sci New Delhi India

In the framework of Granular computing (GC), Interval type 2 Fuzzy Sets (IT2 FSs) play a prominent role by facilitating a better representation of uncertain linguistic information. Perceptual computing (Per-C), a well-known computing with words (CWW) approach, and its various applications have nicely exploited this advantage. This paper reports a novel Per-C-based approach for student strategy evaluation. Examinations are generally oriented to test the subject knowledge of students. The number of questions that they are able to solve accurately judges success rates of students in the examinations. However, we feel that not only the solutions of questions, but also the strategy adopted for finding those solutions are equally important. More marks should be awarded to a student, who solves a question with a better strategy compared to a student, whose strategy is relatively not that good. Furthermore, the student's strategy can be taken as a measure of his/her learning outcome as perceived by a faculty member. This can help to identify students, whose learning outcomes are not good, and, thus, can be provided with any relevant help, for improvement. The main contribution of this paper is to illustrate the use of CWW for student strategy evaluation and present a comparison of the recommendations generated by different CWW approaches. CWW provides us with two major advantages. First, it generates a numeric score for the overall evaluation of strategy adopted by a student in the examination. This enables comparison and ranking of the students based on their performances. Second, a linguistic evaluation describing the student strategy is also obtained from the system. Both these numeric score and linguistic recommendation are together used to assess the quality of a student's strategy. Furthermore, the linguistic recommendation is useful for human beings as they naturally understand and express themselves using 'words', 'words' being treated as fuzzy information granules

关键词： computing with words Extension principle Interval type 2 fuzzy sets Student strategy evaluation Symbolic method Type 1 fuzzy sets 2-Tuple linguistic representation model

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computing with words in Decision support Systems: An overview on Models and Applications

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2010年第4期3卷 382-395页

作者： Martinez, Luis Ruan, Da Herrera, Francisco Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Ghent B-9000 Ghent Belgium CEN SCK Belgian Nucl Res Ctr B-2400 Mol Belgium Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Decision making is inherent to mankind, as human beings daily face situations in which they should choose among different alternatives by means of reasoning and mental processes. Many of these decision problems are under uncertain environments with vague and imprecise information. This type of information is usually modelled by linguistic information because of the common use of language by the experts involved in the given decision situations, originating linguistic decision making. The use of linguistic information in decision making demands processes of computing with words to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish such processes in an accurate and interpretable way. The good performance of linguistic computing dealing with uncertainty has caused a spread use of it in different types of decision based applications. This paper overviews the more significant and extended linguistic computing models due to its key role in linguistic decision making and a wide range of the most recent applications of linguistic decision support models.

关键词： Decision support systems linguistic decision making computing with words

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computing with words for text processing: An approach to the text categorization

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INFORMATION SCIENCES 2006年第4期176卷 415-437页

作者： Zadrozny, S Kacprzyk, J Warsaw Sch Informat Technol PL-01447 Warsaw Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

The use of the computing with words paradigm for the automatic text documents categorization problem is discussed. This specific problem of information retrieval (IR) becomes more and more important, notably in view of a fast proliferation Of textual information available on the Internet. The main issues that have to be addressed here are: document representation and classification. The use of fuzzy logic for both problems has already been quite deeply Studied though for the latter, i.e., classification, generally not in an IR context. Our approach is based mainly on the classical calculus of linguistically quantified propositions proposed by Zadeh. Moreover, we employ results related to fuzzy (linguistic) queries in IR, notably various interpretations of the weights of query terms. Some preliminary results on widely adopted text corpora are presented. (C) 2005 Elsevier Inc. All rights reserved.

关键词： computing with words information retrieval linguistic quantifiers text categorization

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