Abstract
When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.
- [1] . 2012. Individual Values and Social Choice (3rd ed.). Yale University Press, New Haven, CT.Google Scholar
- [2] . 2006. Methodologies and algorithms for group-rankings decision. Manage. Sci. 52, 9 (Sept. 2006), 1394–1408. Google Scholar
Digital Library
- [3] . 2020. Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute large-scale group decision making: A linguistic distribution-based approach. IEEE Trans. Fuzzy Syst. 28, 11 (Nov. 2020), 2875–2889. Google Scholar
Cross Ref
- [4] . 2021. Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. Eur. J. Oper. Res. 288, 1 (Jan. 2021), 271–293. Google Scholar
Cross Ref
- [5] . 2009. Computing with words in decision making: Foundations, trends and prospects. Fuzzy Optim. Decis. Making 8, 4 (Dec. 2009), 337–364. Google Scholar
Digital Library
- [6] . 2021. Revisiting fuzzy and linguistic decision making: Scenarios and challenges for making wiser decisions in a better way. IEEE Trans. Syst. Man Cybern. Syst. 51, 1 (Jan. 2021), 191–208. Google Scholar
Cross Ref
- [7] . 1996. Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 2 (May 1996), 103–111. Google Scholar
Digital Library
- [8] . 2013. A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. Eur. J. Oper. Res. 230, 3 (Nov. 2013), 624–633. Google Scholar
Cross Ref
- [9] . 2021. Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Inf. Fusion 65 (Jan. 2021), 165–178. Google Scholar
Cross Ref
- [10] . 2000. A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8, 6 (Dec. 2000), 746–752. Google Scholar
Digital Library
- [11] . 2010. Perceptual Computing: Aiding People in Making Subjective Judgments. John Wiley & Sons, Hoboken, NJ.Google Scholar
Cross Ref
- [12] . 2017. Personalized individual semantics in computing with words for supporting linguistic group decision making: An application on consensus reaching. Inf. Fusion 33 (Jan. 2017), 29–40. Google Scholar
Digital Library
- [13] . 2022. Integrating continual personalized individual semantics learning in consensus reaching in linguistic group decision making. IEEE Trans. Syst. Man Cybern. Syst. 52, 3 (March 2022), 1525–1536. Google Scholar
Cross Ref
- [14] . 2020. Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making. Inf. Fusion 53 (Jan. 2020), 20–34. Google Scholar
Digital Library
- [15] . 2021. Linguistic opinions dynamics based on personalized individual semantics. IEEE Trans. Fuzzy Syst. 29, 9 (Sept. 2021), 2453–2466. Google Scholar
Digital Library
- [16] . 2021. An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement. Inf. Sci. 575 (Oct. 2021), 499–527. Google Scholar
Digital Library
- [17] . 1988. A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences. Eur. J. Oper. Res. 34, 3 (March 1988), 316–325. Google Scholar
Cross Ref
- [18] . 2018. A self-management mechanism for noncooperative behaviors in large-scale group consensus reaching processes. IEEE Trans. Fuzzy Syst. 26, 6 (Dec. 2018), 3276–3288. Google Scholar
Digital Library
- [19] . 2021. RTChain: A reputation system with transaction and consensus incentives for e-commerce blockchain. ACM Trans. Internet Technol. 21, 1 (Feb. 2021), 1–24. Google Scholar
Digital Library
- [20] . 2014. Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Inf. Fusion 20 (Nov. 2014), 252–271. Google Scholar
Cross Ref
- [21] . 2020. An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Inf. Fusion 60 (Aug. 2020), 65–79. Google Scholar
Cross Ref
- [22] . 2018. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Inf. Fusion 41 (May 2018), 232–242. Google Scholar
Digital Library
- [23] . 2021. Fairness concern: An equilibrium mechanism for consensus-reaching game in group decision-making. Inf. Fusion 72 (Aug. 2021), 147–160. Google Scholar
Cross Ref
- [24] . 2020. The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Inf. Fusion 63 (Nov. 2020), 88–120. Google Scholar
Cross Ref
- [25] . 2008. A model and case for supporting participatory public decision making in e-democracy. Group Decis. Negot. 17, 3 (May 2008), 179–193. Google Scholar
Cross Ref
- [26] . 2014. A consensus model to detect and manage noncooperative behaviors in large-scale group decision making. IEEE Trans. Fuzzy Syst. 22, 3 (June 2014), 516–530. Google Scholar
Digital Library
- [27] . 2020. Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmos. Pollut. Res. 11, 1 (Jan. 2020), 40–56. Google Scholar
Cross Ref
- [28] . 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, NY.Google Scholar
Cross Ref
- [29] . 2020. Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective. Inf. Fusion 59 (July 2020), 84–102. Google Scholar
Cross Ref
- [30] . 2018. A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Comput. Ind. Eng. 116 (Feb. 2018), 113–129. Google Scholar
Cross Ref
- [31] . 2019. A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups. IEEE Trans. Fuzzy Syst. 27, 2 (Feb. 2019), 221–233. Google Scholar
Cross Ref
- [32] . 2018. Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions. Knowl. Based Syst. 145 (April 2018), 156–165. Google Scholar
Digital Library
- [33] . 2020. Personalized review recommendation based on users’ aspect sentiment. ACM Trans. Internet Technol. 20, 4 (Nov. 2020), 1–26. Google Scholar
Digital Library
- [34] . 2009. Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Trans. Fuzzy Syst. 17, 6 (Dec. 2009), 1366–1378. Google Scholar
Digital Library
- [35] . 1978. A fuzzy relation space for group decision theory. Fuzzy Set. Syst. 1, 4 (Oct. 1978), 255–268. Google Scholar
Cross Ref
- [36] . 2004. Some issues on consistency of fuzzy preference relations. Eur. J. Oper. Res. 154, 1 (April 2004), 98–109. Google Scholar
Cross Ref
- [37] . 2008. A consistency-based procedure to estimate missing pairwise preference values. Int. J. Intell. Syst. 23, 2 (Feb. 2008), 155–175. Google Scholar
Cross Ref
- [38] . 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 301 (1963), 236–244. Google Scholar
Cross Ref
- [39] . 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability 5.1 (Jan 1967), 281–297.Google Scholar
- [40] . 1963. Linear Programming and Extensions. Princeton University Press, Princeton, NJ.Google Scholar
- [41] . 2020. Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58 (June 2020), 52–68. Google Scholar
Digital Library
- [42] . 2017. IoT middleware: A survey on issues and enabling technologies. IEEE Internet Things J. 4, 1 (Feb. 2017), 1–20. Google Scholar
Cross Ref
- [43] . 2016. Things of interest recommendation by leveraging heterogeneous relations in the Internet of Things. ACM Trans. Internet Technol. 16, 2 (April 2016), 1–25. Google Scholar
Digital Library
- [44] . 2021. A survey on IoT big data: Current status, 13 V's challenges, and future directions. ACM Comput. Surv. 53, 6 (Feb. 2021), 1–59. Google Scholar
Digital Library
- [45] . 2019. Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: Detecting and managing overconfidence behaviors. Inf. Fusion 52 (Dec. 2019), 245–256. Google Scholar
Digital Library
- [46] . 2012. Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20, 1 (Feb. 2012), 109–119. Google Scholar
Digital Library
- [47] . 2014. Consistency and consensus measures for linguistic preference relations based on distribution assessments. Inf. Fusion 17 (May 2014) 46–55. Google Scholar
Digital Library
Index Terms
Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process
Recommendations
Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions
AbstractIn decision making problems, decision makers may prefer to use more flexible linguistic expressions instead of using only one linguistic term to express their preferences. The recent proposals of hesitant fuzzy linguistic terms sets (...
Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching
To propose a personalized individual semantics model (PIS).To propose personalized 2-tuple linguistic comparison and aggregation.To discuss the application of PIS to support linguistic consensus reaching. In group decision making (GDM) dealing with ...
Learning personalized individual semantics through the data of distributed linguistic preference relations: A two-stage method to support linguistic consensus reaching
Highlights- We propose a two-stage consensus approach in a distributed linguistic context.
- ...
AbstractDistribution linguistic preference relation (DLPR) is an effective tool to model linguistic preferences with multiple linguistic terms, and has widespread adoption in the linguistic group decision making (GDM), in which we commonly ...






Comments