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1
Some Trends in Fuzzy Decision Making
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2
Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
Abstract: The file attached to this record is the author's final peer reviewed version. ; In linguistic decision making problems there may be cased when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, i.e. decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this paper we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs based incomplete linguistic preference estimation method.
Keyword: consistency; incomplete linguistic preference relation; linguistic decision making; Personalized individual semantics
URL: https://doi.org/10.1109/tcyb.2021.3072147
https://dora.dmu.ac.uk/handle/2086/20804
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3
Consensus in Sentiment Analysis
Carter, Jenny; Chiclana, Francisco; Fujita, Hamido. - : Springer International Publishing, 2021
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4
Sentiment Analysis using TF-IDF Weighting of UK MPs’ Tweets on Brexit
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5
Proportional hesitant 2-tuple linguistic distance measurements and extended VIKOR method: Case study of evaluation and selection of green airport plans
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6
A Fuzzy Approach to Sentiment Analysis at the Sentence Level
Appel, Orestes; Chiclana, Francisco; Carter, Jenny. - : Springer International Publishing, 2021
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7
Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events
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8
Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis.
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9
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
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10
Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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11
ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages [<Journal>]
Guellil, Imane [Verfasser]; Azouaou, Faical [Verfasser]; Chiclana, Francisco [Verfasser]
DNB Subject Category Language
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12
Attitude Quantifier Based Possibility Distribution Generation Method for Hesitant Fuzzy Linguistic Group Decision Making
Hao, Jingjing; Chiclana, Francisco. - : Elsevier, 2020
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13
Multi-stage consistency optimization algorithm to decision-making with incomplete probabilistic linguistic preference relation
Wang, Peng; Liu, Peide; Chiclana, Francisco. - : Elsevier, 2020
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14
Revisiting Fuzzy and Linguistic Decision-Making: Scenarios and Challenges for Wiser Decisions in a Better Way
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15
ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages
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16
Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
Dong, Yucheng; Zhang, Hengjie; Chiclana, Francisco. - : Taylor & Francis Group, 2020
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17
Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts
Zhang, Hengjie; Dong, Yucheng; Xiao, Jing. - : Elsevier, 2020
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18
Type-1 OWA operators in aggregating multiple sources of uncertain information : properties and real world applications
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19
Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. &#x27E8;10.1002/int.22095&#x27E9; (2019)
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20
Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects
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