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Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
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Sentiment Analysis using TF-IDF Weighting of UK MPs’ Tweets on Brexit
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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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A Fuzzy Approach to Sentiment Analysis at the Sentence Level
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Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events
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Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis.
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Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
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Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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| 12 |
Attitude Quantifier Based Possibility Distribution Generation Method for Hesitant Fuzzy Linguistic Group Decision Making
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| 13 |
Multi-stage consistency optimization algorithm to decision-making with incomplete probabilistic linguistic preference relation
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Abstract:
The file attached to this record is the author's final peer reviewed version. ; Incomplete probabilistic linguistic term sets (InPLTSs) can effectively describe the qualitative pairwise judgment information in uncertain decision-making problems, making them suitable for solving real decision-making problems under time pressure and lack of knowledge. Thus, in this study, an optimization algorithm is developed for preference decision-making with the incomplete probabilistic linguistic preference relation (InPLPR). First, to fully investigate the ability of InPLTSs to express uncertain information, they are divided into two categories. Then, a two-stage mathematical optimization model based on an expected multiplicative consistency for estimating missing information is constructed, which can obtain the complete information more scientifically and effectively than some exiting methods. Subsequently, for the InPLPR with unacceptable consistency, a multi-stage consistency-improving optimization model is proposed for improving the consistency of the InPLPR by minimizing the information distortion and the number of adjusted linguistic terms, which can also minimize the uncertainty of the relationship as small as possible. Afterward, to rank all the alternatives, a mathematical model for deriving the priority weights of the alternatives is constructed and solved, which can obtain the priority weight conveniently and quickly. A decision-making algorithm based on the consistency of the InPLPR is developed, which involves estimating missing information, checking and improving the consistency, and ranking the alternatives. Finally, a numerical case involving the selection of excellent students is presented to demonstrate the application of the proposed algorithm, and a detailed validation test and comparative analysis are presented to highlight the advantages of the proposed algorithm.
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Keyword:
excellent student selection; expected multiplicative consistency; incomplete probabilistic linguistic preference relation; mathematical optimization model; preference decision-making
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URL: https://dora.dmu.ac.uk/handle/2086/20296
https://doi.org/10.1016/j.ins.2020.10.004
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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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ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages
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Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
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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
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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
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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. ⟨10.1002/int.22095⟩ (2019)
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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects
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