The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—II
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Fuzzy set theory has been recognized as an effective framework for addressing the uncertainty and imprecision inherent in linguistic assessments, thereby providing substantial support for decision-making processes.
“For more than half a century, fuzzy set theory has been recognized as an effective framework for addressing the uncertainty and imprecision inherent in linguistic assessments, thereby providing substantial support for decision-making processes [47].”
From An Integrated AHP–Fuzzy AHP Evaluation Framework for Large Language Models in Software Engineering Education (2026) · cites this paper as [47]
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Zadeh, L. (1997) · Fuzzy Sets Syst. · doi:10.1016/s0165-0114(97)00077-8 · Cited by 2,771 · see citing works
“…paper on fuzzy sets [24]; 1971 paper on fuzzy systems [26]; 1973-1976 papers on linguistic variables, fuzzy if-then rules and fuzzy graphs [27] [28] [29] [30]; 1979 paper on fuzzy sets and information granularity [31]; 1986 paper on generalized constraints [32] and 1996 paper on computing with…”
Self-citationZadeh, L. (1999) · Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296) · doi:10.1111/j.1749-6632.2001.tb05718.x · Cited by 1,671
Self-citationLinguistic decision analysis: steps for solving decision problems under linguistic information
Herrera, F., & Herrera-Viedma, E. (2000) · Fuzzy Sets Syst. · doi:10.1016/s0165-0114(99)00024-x · Cited by 1,644 · see citing works
“Thus, it is established by means of two elements: (i) the primary fuzzy sets associated to the primary linguistic terms and (ii) a semantic rule M for generating the fuzzy sets of the non-primary linguistic terms from primary fuzzy sets.”
InfluentialIs There a Need for Fuzzy Logic?
Zadeh, L. A. (2008) · Information Sciences · doi:10.1016/j.ins.2008.02.012 · Cited by 1,352 · see citing works
Self-citationA model of consensus in group decision making under linguistic assessments
Herrera, F., Herrera-Viedma, E., & Verdegay, J. (1996) · Fuzzy Sets Syst. · doi:10.1016/0165-0114(95)00107-7 · Cited by 1,223
A fusion approach for managing multi-granularity linguistic term sets in decision making
Herrera, F., Herrera-Viedma, E., & Martínez, L. (2000) · Fuzzy Sets Syst. · doi:10.1016/s0165-0114(98)00093-1 · Cited by 845 · see citing works
Managing non-homogeneous information in group decision making
Herrera, F., Mart́ınez, L., & Sánchez, P. J. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2003.11.031 · Cited by 637 · see citing works
“The fuzzy linguistic approach represents the information as linguistic values by means of linguistic variables [34].”
Toward a generalized theory of uncertainty (GTU) - an outline
Zadeh, L. (2005) · IEEE International Conference on Granular Computing · doi:10.1109/grc.2005.1547227 · Cited by 634
“The concept of granularity underlies the concept of a linguistic variable––a concept which was introduced in my 1973 paper ''Outline of A New Approach to the Analysis of Complex Systems and Decision Processes'' [41] [42].”
Self-citationA Sequential Selection Process in Group Decision Making with a Linguistic Assessment Approach
Herrera, F., Herrera-Viedma, E., & Verdegay, J. (1995) · Information Sciences · doi:10.1016/0020-0255(95)00025-k · Cited by 631
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“The compatibility function is in fact the membership function in Zadeh’s fuzzy set theory [51].”
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Zwick, R., Carlstein, E., & Budescu, D. (1987) · International Journal of Approximate Reasoning · doi:10.1016/0888-613x(87)90015-6 · Cited by 543 · see citing works
Computing with words in decision making: foundations, trends and prospects
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Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity
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Measuring the Vague Meanings of Probability Terms
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“Assuming that the monotonic shapes are not artifactual, an aid to interpreting the differences between the two types of functions may come from considering functions empirically determined in other areas involving linguistic variables over numerical domains (Zadeh, 1975).”
BackgroundMethodInfluentialGeneralized rough sets based on relations
Zhu, W. (2007) · Information Sciences · doi:10.1016/j.ins.2007.05.037 · Cited by 451
Trust based consensus model for social network in an incomplete linguistic information context
Wu, J., Chiclana, F., & Herrera-Viedma, E. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.02.023 · Cited by 399
“In these cases is still valid the following quotation by Zadeh [62]: 'Since words, in general, are less precise than numbers, the concept of a linguistic variable serves the purpose of providing a means of approximate characterisation of phenomena which are too complex or too ill-defined to be amenable to description…”
BackgroundGeneralized theory of uncertainty (GTU) - principal concepts and ideas
Zadeh, L. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.04.029 · Cited by 388 · see citing works
Self-citationIntegration of featural information in speech perception.
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“Since acoustic features vary continuously from one speech sound to another, they can be represented as predicates that may be more or less true rather than only absolutely true or false (Goguen, 1969; Zadeh, 1975).”
BackgroundPythagorean Fuzzy Information Measures and Their Applications
Peng, X.-D., Yuan, H., & Yang, Y. (2017) · International Journal of Intelligent Systems · doi:10.1002/int.21880 · Cited by 344 · see citing works
Awasthi, A., Chauhan, S., & Goyal, S. K. (2011) · Mathematical and computer modelling · doi:10.1016/j.mcm.2010.07.023 · Cited by 343
“In such systems, parameters are defined using linguistic terms instead of exact numerical values [4].”
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Fuzzy decision making: A bibliometric-based review
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A Fuzzy Linguistic Approach Generalizing Boolean Information Retrieval: A Model and Its Evaluation
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“A linguistic variable is a variable whose values are words or sentences in a natural or artificial language (Zadeh, 1975).”
BackgroundCabrerizo, F., Pérez, I. J., & Herrera-Viedma, E. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.11.019 · Cited by 310
“In these cases, the fuzzy linguistic approach [19] [34] [41] [49] [50] [55] [56] [57] can be used to obtain a better solution.”
Zhang, Z., Guo, C., & Martínez, L. (2017) · IEEE Transactions on Systems, Man & Cybernetics. Systems · doi:10.1109/tsmc.2016.2560521 · Cited by 309 · see citing works
Ding, R.-X., Palomares, I., Wang, X., Yang, G.-R., Liu, B., Dong, Y., Herrera-Viedma, E., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.006 · Cited by 301 · see citing works
Cabrerizo, F., Herrera-Viedma, E., & Pedrycz, W. (2013) · European Journal of Operational Research · doi:10.1016/j.ejor.2013.04.046 · Cited by 260
Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic
Sepúlveda, R., Castillo, O., Melin, P., Rodríguez-Díaz, A., & Montiel, O. (2007) · Information Sciences · doi:10.1016/j.ins.2006.10.004 · Cited by 259
“When something is uncertain, like a measurement, it is difficult to determine its exact value, and of course type-1 fuzzy sets make more sense than using sets [42,43].”
Personnel selection based on computing with words and fuzzy MULTIMOORA
Balezentis, A., Baležentis, T., & Brauers, W. (2012) · Expert systems with applications · doi:10.1016/j.eswa.2012.01.100 · Cited by 248
A Consensus Model for Group Decision Making Problems with Unbalanced Fuzzy Linguistic Information
Cabrerizo, F., Alonso, S., & Herrera-Viedma, E. (2009) · International Journal of Information Technology and Decision Making · doi:10.1142/s0219622009003296 · Cited by 245
A linguistic consensus model for Web 2.0 communities
Alonso, S., Pérez, I. J., Cabrerizo, F., & Herrera-Viedma, E. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2012.08.009 · Cited by 244 · see citing works
“Other models allow experts to use multiple preference structures (and even multi-granular linguistic information) [13] [14] [15] and other different approaches deal with incomplete information situations if experts are not able to provide all their preferences when solving a GDM problem [16] or…”
An application of the fuzzy ELECTRE method for supplier selection
Şevkli, M. (2009) · International Journal of Production Research · doi:10.1080/00207540902814355 · Cited by 248 · see citing works
“2.1 Linguistic variables and fuzzy sets A linguistic variable is a variable whose values are words or sentences in a natural or artificial language (Zadeh 1975a).”
BackgroundInfluentialMorente-Molinera, J. A., Pérez, I. J., Ureña, R., & Herrera-Viedma, E. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.11.001 · Cited by 234
Torlak, G., Sevkli, M., Sanal, M., & Zaim, S. (2011) · Expert systems with applications · doi:10.1016/j.eswa.2010.08.125 · Cited by 228
“The main applications of the linguistic approach lie in the realm of humanistic systems especially in the fields of artificial intelligence, linguistics, human decision processes, pattern recognition, psychology, law, medical diagnosis, information retrieval, economics, and related areas (Bellman & Zadeh, 1970; Zadeh,…”
BackgroundTurskis, Z., & Zavadskas, E. K. (2010) · Transport · doi:10.3846/transport.2010.52 · Cited by 248 · see citing works
“Zadeh (1965, 1971, 1975a–c) made a crucial step in the design of a problem solving tool.”
BackgroundAn analysis of symbolic linguistic computing models in decision making
Rodríguez, R. M., & Martínez, L. (2013) · International Journal of General Systems · doi:10.1080/03081079.2012.710442 · Cited by 221
“Initially, two computational models were developed to perform linguistic computations based on the fuzzy linguistic approach (Zadeh 1975a,b,c): • Linguistic computing model based on membership functions (Semantic model) : This model carries out the computations with linguistic terms by operating…”
InfluentialInformation sciences 1968-2016: A retrospective analysis with text mining and bibliometric
Yu, D., Xu, Z.-S., Pedrycz, W., & Wang, W. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.031 · Cited by 210
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Toward extended fuzzy logic - A first step
Zadeh, L. (2009) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2009.04.009 · Cited by 208 · see citing works
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“variables whose values are propositions and words drawn from a natural language [30], [31], [32].”
BackgroundA review of applications of fuzzy sets to safety and reliability engineering
Kabir, S., & Papadopoulos, Y. (2018) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2018.05.005 · Cited by 189 · see citing works
PROJECT TEAM SELECTION USING FUZZY OPTIMIZATION APPROACH
Baykasoğlu, A., Dereli, T., & Das, S. (2007) · Cybernetics and systems · doi:10.1080/01969720601139041 · Cited by 182
“A linguistic variable differs from a numerical variable in that its values are not numbers, but words or sentences in natural or artificial language (Zadeh 1975).”
BackgroundReduction about approximation spaces of covering generalized rough sets
Yang, T., & Li, Q.-G. (2010) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2009.11.001 · Cited by 177
García, J. M. T., Moral, M. J. D., Martínez, M. Á., & Herrera-Viedma, E. (2012) · Expert systems with applications · doi:10.1016/j.eswa.2012.02.008 · Cited by 162
“…& Herrera-Viedma, 2010b, 2010c; Herrera & Herrera-Viedma, 2000a, 2000b; Herrera- Viedma, Martínez, Mata, & Chiclana, 2005; Herrera, Herrera-Viedma, & Martinez, 2008; Mata, Martínez, & Herrera-Viedma, 2009; Xu, 2005, 2007; Zadeh, 1975a, 1975b, 1975c) can be used to obtain a better solution.”
An Interval Fuzzy Controller for Vehicle Active Suspension Systems
Cao, J.-T., Li, P., & Liu, H.-H. (2010) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2010.2053358 · Cited by 161
“The concept of the type-2 fuzzy set was originally introduced by Zadeh [15] as an extension of the ordinary fuzzy set.”
Extended hedge algebras and their application to fuzzy logic
Nguyen, H. C., & Wechler, W. (1992) · doi:10.1016/0165-0114(92)90237-x · Cited by 160
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Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making
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On Three Types of Covering-Based Rough Sets
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“In order to deal with the incomplete and vague information in classification, concept formulation, and data analysis, researchers have proposed many methods other than classical logic, for example, fuzzy set theory [53], rough set theory [21], [22], [23], [24], computing with words [26], [38], [54], [55], [56], [57],…”
Formal Representations of Uncertainty
Dubois, D., & Prade, H. (2010) · Decision-making Process · doi:10.1002/9780470611876.ch3 · Cited by 150
“Zadeh [130, 131, 132] introduced the notion of linguistic variable ranging in a finite ordered set of linguistic terms.”
The IF THEN ELSE Statement and Interval-Valued Fuzzy Sets of Higher Type
Hisdal, E. (1981) · Int. J. Man Mach. Stud. · doi:10.1016/s0020-7373(81)80051-x · Cited by 148
Selecting Green Supplier of Thermal Power Equipment by Using a Hybrid MCDM Method for Sustainability
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“Linguistic variable refers to the variable whose value is a word or a sentence rather than numeral in a natural or artificial language [18].”
Liu, P.-D., & Liu, W. (2018) · International Journal of Intelligent Systems · doi:10.1002/int.22071 · Cited by 144 · see citing works
“which completes the proof of Equation (17).”
On the relevance of some families of fuzzy sets
Montero, J., Gómez, D., & Bustince, H. (2007) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2007.04.021 · Cited by 142
Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing
Kovac, P., Rodić, D., Pucovsky, V., Savković, B., & Gostimirović, M. (2012) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-012-0623-z · Cited by 140 · see citing works
“The theory of fuzzy logics, initiated by Zadeh (1975) has proved to be useful for dealing with uncertain and vague information.”
Some aspects of intuitionistic fuzzy sets
Yager, R. (2009) · Fuzzy Optimization and Decision Making · doi:10.1007/s10700-009-9052-7 · Cited by 133
“When using fuzzy sets to model multiple criteria decision-making problems, as suggested by Bellman and Zadeh (1970), we can represent the criteria as fuzzy subsets over the set of alternatives X.”
MethodYe, J. (2015) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-141295 · Cited by 132
Pelissari, R., Oliveira, M. C., Abackerli, A., Benamor, S., & Assumpção, M. P. (2018) · International Transactions in Operational Research · doi:10.1111/itor.12598 · Cited by 130
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“Linguistic variables expressed in fuzzy numbers were introduced by Zadeh (1975a, 1975b, 1975c) and applied in many studies (Liang 1999; Chen 2000; Chou et al. 2008; Torlak et al. 2011).”
Kaur, G., & Garg, H. (2018) · Entropy · doi:10.3390/e20010065 · Cited by 125
“Thus, the researchers have represented the information in terms of fuzzy sets (FSs) [1], interval-valued fuzzy sets (IVFSs) [2], intuitionistic fuzzy sets (IFSs) [3], interval-valued intuitionistic fuzzy sets (IVIFSs) [4].”
BackgroundA Fast Geometric Method for Defuzzification of Type-2 Fuzzy Sets
Coupland, S., & John, R. (2008) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2008.924345 · Cited by 124
Cázarez-Castro, N., Aguilar, L., & Castillo, O. (2012) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2012.03.003 · Cited by 123
Consistency of hesitant fuzzy linguistic preference relations: An interval consistency index
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“Introduction Solving a decision problem with linguistic information implies the need for computing with words (CW)[11, 23, 24, 35, 36, 37].”
BackgroundLiu, P.-D., & Liu, W. (2019) · International Journal of Intelligent Systems · doi:10.1002/int.22114 · Cited by 114 · see citing works
“i q i q i q i q q i q q i q i q q i q i q i q i q i q i q i q i q (20)”
Fermatean fuzzy linguistic set and its application in multicriteria decision making
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Some consistency measures of extended hesitant fuzzy linguistic preference relations
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The relationship between similarity measure and entropy of intuitionistic fuzzy sets
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Wei, G., Wei, C., & Guo, Y.-F. (2021) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-021-05842-x · Cited by 110
“Especially, the hesitant fuzzy linguistic term sets (HFLTSs) are came up with by Rodriguez et al. (2012) on the foundation of HFSs (Torra 2010) and LTSs (Zadeh 1975) used to allow DMs to give a couple of possible linguistic variable.”
MethodTODIM-based multi-criteria decision-making method with hesitant fuzzy linguistic term sets
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“In this case, a fuzzy linguistic method was developed in Zadeh (1975) so as to express the qualitative information.”
MethodMulti-criteria decision-making based on hesitant fuzzy linguistic term sets: An outranking approach
Wang, J., Wang, J.-Q., Zhang, H.-Y., & Chen, X.-H. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.007 · Cited by 108
“The fuzzy linguistic approach [3,9,10] was proposed during the 1970s, and has since received popular recognition.”
BackgroundMethodA brief review of modeling approaches based on fuzzy time series
Singh, P. (2015) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-015-0332-y · Cited by 101 · see citing works
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“1) Herrera and Martı́nez Model: The basic notations and operational laws of linguistic variables are introduced in [24], [26]–[28], and [66].”
BackgroundReview of multinomial and multiattribute quality control charts
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Fuzzy dual-factor time-series for stock index forecasting
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“Song and Chissom (1993) first applied the fuzzy theory (Zadeh, 1965; Zadeh, 1975a; Zadeh, 1975b; Zadeh, 1976) in time-series and provided the definitions and framework of fuzzy time-series, where time-series data are represented by fuzzy sets instead of crisp values.”
BackgroundRisk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach
Liu, H.-C., Chen, Y.-Z., You, J.-X., & Li, H. (2014) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-014-0915-6 · Cited by 99 · see citing works
“(6) A linguistic variable is defined as a variable whose values are expressed in linguistic terms (Zadeh 1975a).”
BackgroundA New Type of Covering Rough Set
Zhu, W., & Wang, F.-Y. (2006) · 2006 3rd International IEEE Conference Intelligent Systems · doi:10.1109/is.2006.348460 · Cited by 97
Lu, J., Wei, C., Wu, J., & Wei, G. (2019) · Entropy · doi:10.3390/e21100953 · Cited by 95
Management of Uncertainty and Spatio-Temporal Aspects for Monitoring and Diagnosis in a Smart Home
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“Following that, a more rigorous analysis was undertaken with respect to accuracy by means of the Wilcoxon Nonparametric Test, in order to validate the preliminary test conclusions.”
Morente-Molinera, J. A., Kou, G., Pang, C., Cabrerizo, F., & Herrera-Viedma, E. (2019) · Information Sciences · doi:10.1016/j.ins.2018.10.022 · Cited by 92 · see citing works
Supplier Selection of Medical Consumption Products with a Probabilistic Linguistic MABAC Method
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“then the conditions f s μ x f s μ x ( ) ( ( )) − ( ) ( ( )) = 0, * * θ x L j θ x L j ( ) 3 ( ) 3 L j L j 1 1 2 2 ⋅ ⋅ (9)”
A Solution Based on Bluetooth Low Energy for Smart Home Energy Management
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“Fuzzy logic controllers (FLCs) [30] are becoming increasingly popular in everyday life.”
BackgroundThe sampling method of defuzzification for type-2 fuzzy sets: Experimental evaluation
Greenfield, S., Chiclana, F., John, R., & Coupland, S. (2012) · Information Sciences · doi:10.1016/j.ins.2011.11.042 · Cited by 88 · see citing works
Human Gait Modeling Using a Genetic Fuzzy Finite State Machine
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A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals
Castro-Schez, J., Miguel, R., Vallejo, D., & López-López, L. M. (2011) · Expert systems with applications · doi:10.1016/j.eswa.2010.08.033 · Cited by 87
“Fuzzy logic (Zadeh, 1965; Zadeh, 1975) is a well validated tool to handle vagueness and uncertainty in information.”
Zuheros, C., Martínez‐Cámara, E., Herrera‐Viedma, E., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.019 · Cited by 97 · see citing works
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
Mencar, C., Castiello, C., Cannone, R., & Fanelli, A. (2011) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2010.11.007 · Cited by 86
“2 For the sake of simplicity we do not take into account the generative grammar for the linguistic values, as originally proposed by Zadeh in [52].”
Zhang, B.-W., Liang, H., Gao, Y., & Zhang, G. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.038 · Cited by 85
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Song, Y., & Hu, J.-H. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.04.036 · Cited by 83
Relationships among three types of covering rough sets
Zhu, W., & Wang, F.-Y. (2006) · IEEE International Conference on Granular Computing · doi:10.1109/grc.2006.1635755 · Cited by 83
“set theory [1], rough set theory [2], computing with words [3], [4], [5], [6], [7], granular computing [8], [9], [10], [11], [12], computational theory for linguistic dynamic systems [3], etc.”
BackgroundA behavioural model for vague probability assessments
de Cooman, G. (2005) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2005.01.005 · Cited by 83
“In Zadeh’s work on fuzzy probabilities [47, 48, 49, 52] the so-called fuzzy expectation of a real random variable X on a finite spaceΩ = {ω1, .”
BackgroundTan, X.-M., Zhu, J., Cabrerizo, F., & Herrera-Viedma, E. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106937 · Cited by 82 · see citing works
Wei, G., Lei, F., Lin, R., Wang, R., Wei, Y., Wu, J., & Wei, C. (2020) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2020.1734851 · Cited by 82
“Furthermore, Rodriguez, Martinez, and Herrera (2012) proposed the hesitant fuzzy linguistic term sets (HFLTSs) on the basis of hesitant fuzzy sets(Torra, 2010) and linguistic term sets (LTSs) (Zadeh, 1975) which allowed DMs to provide some possible linguistic information.”
BackgroundMapping development of linguistic decision making studies
Yu, D., Li, D.-F., Merigó, J., & Fang, L. (2016) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-152026 · Cited by 82
“Zadeh (UC Berkeley, USA), Herrera (University of Granada, Spain), Yager (Iona College, USA), Xu (Sichuan University, China) and Herrera-Viedma (University of Granada, Spain) are the most influential scholars in this field.”
BackgroundInfluentialWang, J., Wang, J.-Q., Zhang, H.-Y., & Chen, X.-H. (2015) · International Journal of Fuzzy Systems · doi:10.1007/s40815-015-0050-3 · Cited by 80
“In contrast, Rodriguez et al. combined the fuzzy linguistic approach [2, 24] with hesitant fuzzy sets (HFSs) [25] in which several values were used to define the membership of an element, and proposed hesitant fuzzy linguistic term sets (HFLTSs) [26] that consist of discrete linguistic terms.”
Background
- Authors
- Zadeh, L. A.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Zadeh, L.A.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 1975-01
- Volume
- 8
- Issue
- 4
- Pages
- 301-357
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4240278385
- Cited by
- 2,555
- References
- 0
- Citations by year
- 2026: 42 · 2025: 72 · 2024: 83 · 2023: 106 · 2022: 135 · 2021: 130 · 2020: 134 · 2019: 133 · 2018: 122 · 2017: 100 · 2016: 115 · 2015: 136 · 2014: 95 · 2013: 83 · 2012: 69
- FWCI
- 13.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Sciences (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Social Sciences › Decision Sciences › Management Science and Operations Research › Fuzzy and Soft Set Theory
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
- Keywords
- ELECTRE II, multi-criteria group decision-making, outranking relations, fuzzy ELECTRE, linguistic variables, approximate reasoning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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