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A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.
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Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
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An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions
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Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management
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A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
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Preference similarity network structural equivalence clustering based consensus group decision making model
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An interaction consensus in group decision making under distributed trust information
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29 |
Successes and challenges in developing a hybrid approach to sentiment analysis
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A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust
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A consensus approach to the sentiment analysis problem driven by support-based IOWA majority
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Type-1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology. Application to Eurobonds Credit Risk Evaluation
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Preference Similarity Network Structural Equivalence Clustering based Consensus Model
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Abstract:
Open access article ; Social network analysis (SNA) methods have been developed to analyse social structures and patterns of network relationships, although they have been least explored and/or exploited purposely for decision-making processes. In this study, we bridge a gap between SNA and consensus-based decision making by defining undirected weighted preference network from the similarity of expert preferences using the concept of ‘structural equivalence’. Structurally equivalent experts are represented using the agglomerative hierarchical clustering algorithm with complete link function, thus intra-clusters’ experts are high in density and inter-clusters’ experts are rich in sparsity. We derive cluster consensus based on internal and external cohesions, while group consensus is obtained by identifying the highest level consensus at optimal level of clustering. Thus, the clustering based approach to consensus measure contributes to present homogeneity of experts preferences as a whole. In the event of insufficient group consensus state, we construct a feedback mechanism procedure based on clustering that consists of three main phases: (1) identification of experts that contribute less to consensus; (2) identification of a leader in the network; and (3) advice generation. We make use of the centrality concept in SNA as a way of determining the most important person in a network, who is presented as a leader to provide advices in the feedback process. It is proved that the implementation of the proposed feedback mechanism increases consensus and, because of the bounded condition of consensus measure, convergence to sufficient group agreement is guaranteed. The centrality concept is also applied in the construction of a new aggregation operator, namely as cent-IOWA operator, that is used to derive the collective preference relation from which the feasible alternative of consensus solution, based on the concept of dominance, is achieved according to a majority of the central experts in the network, which is represented in this paper by the linguistic quantifier ‘most of.’ For validation purposes, an existing literature study is used to perform a comparative analysis from which conclusions are drawn and explained.
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Keyword:
Agglomerative Hierarchical Clustering; Consensus Group Decision Making; IOWA-based aggregation operator; Opinion Similarity; Social Network Analysis; Structural Equivalence
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URL: http://hdl.handle.net/2086/14943 https://doi.org/10.1016/j.asoc.2017.11.022
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34 |
Linguistic multi-criteria decision-making model with output variable expressive richness
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35 |
Successes and challenges in developing a hybrid approach to sentiment analysis
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36 |
A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority
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38 |
A Hybrid Approach to Sentiment Analysis with Benchmarking Results
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A new consensus measure based on Pearson correlation coefficient
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A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level
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