Author
David Pérez-Román
Bio: David Pérez-Román is an academic researcher from University of Valladolid. The author has contributed to research in topics: Cluster analysis & Measure (mathematics). The author has an hindex of 9, co-authored 17 publications receiving 248 citations.
Papers
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01 Jan 2011TL;DR: A new class of consensus measures on weak orders based on distances is introduced, and some of their properties are analyzed paying special attention to seven well-known distances.
Abstract: In this chapter we focus our attention in how to measure consensus in groups of agents when they show their preferences over a fixed set of alternatives or candidates by means of weak orders (complete preorders). We have introduced a new class of consensus measures on weak orders based on distances, and we have analyzed some of their properties paying special attention to seven well-known distances.
50 citations
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TL;DR: An agglomerative hierarchical clustering process where the clusters of agents are generated by using a distance-based consensus measure to judge the feasible alternatives through linguistic terms.
40 citations
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TL;DR: This paper proposes a measure of consensus which is robust to some extensions of the ordinal framework and shows that there exists a limit for increasing the homogeneity level in a group of individuals by simply replicating their preference-approvals.
37 citations
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01 Oct 2015TL;DR: This paper introduces ordinal proximity measures in the setting of unbalanced qualitative scales by comparing the proximities between linguistic terms without numbers in a purely ordinal approach and proposes an agglomerative hierarchical clustering procedure based on these consensus measures.
Abstract: In this paper, we introduce ordinal proximity measures in the setting of unbalanced qualitative scales by comparing the proximities between linguistic terms without numbers, in a purely ordinal approach. With this new tool, we propose how to measure the consensus in a set of agents when they assess a set of alternatives through an unbalanced qualitative scale. We also introduce an agglomerative hierarchical clustering procedure based on these consensus measures.
30 citations
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01 Nov 2010TL;DR: The class of weighted Kemeny distances on weak orders is introduced for taking into account where the disagreements occur, and the properties of the associated consensus measures are analyzed.
Abstract: In this paper we analyze the consensus in groups of decision makers that rank alternatives by means of weak orders. We have introduced the class of weighted Kemeny distances on weak orders for taking into account where the disagreements occur, and we have analyzed the properties of the associated consensus measures.
25 citations
Cited by
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TL;DR: A LGDM consensus model in which the clusters are allowed to change and the decision makers provide preferences using fuzzy preference relations is proposed, and an emergency decision to choose a rescue plan is illustrated to validate the proposed method and demonstrate distinctive characteristics compared with the existing approaches.
278 citations
01 Jan 2016
TL;DR: Thank you very much for reading fuzzy multiple attribute decision making methods and applications, as people have look numerous times for their chosen readings, but end up in malicious downloads.
Abstract: Thank you very much for reading fuzzy multiple attribute decision making methods and applications. As you may know, people have look numerous times for their chosen readings like this fuzzy multiple attribute decision making methods and applications, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their desktop computer.
230 citations
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TL;DR: A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems.
Abstract: In linguistic large-scale group decision making (LSGDM), it is often necessary to achieve a consensus. Particularly, when computing with words and linguistic decision, we must keep in mind that words mean different things to different people. Therefore, to represent the specific semantics of each individual, we need to consider the personalized individual semantics (PIS) model in linguistic LSGDM. In this paper, we propose a consensus model based on PIS for LSGDM. Specifically, a PIS process to obtain the individual semantics of linguistic terms with linguistic preference relations is introduced. A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems. The consensus measure defines two opposing consensus groups with respective acceptable and unacceptable consensus. In the feedback recommendation phase, a PIS-based clustering method to get decision makers with similar individual semantics is proposed. Recommendation rules design a feedback for decision makers with unacceptable consensus, finding suitable moderators from the decision makers with acceptable consensus based on cluster proximity.
216 citations
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TL;DR: This position paper studies the necessity of hesitant fuzzy sets and provides a discussion about current proposals including a guideline that the proposals should follow and some challenges of HFSs.
212 citations
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TL;DR: A comprehensive review regarding the different approaches to CRP is reported, and a series of CRPs as the comparison objects are presented, and the following comparison criteria for measuring the efficiency of CPRs are proposed.
212 citations