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Prof. Dr. Enrique Herrera-Viedma
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain

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Research Keywords & Expertise

0 Information Retrieval
0 Recommender Systems
0 Intelligent decision-making
0 Group decision-making
0 Consensus models

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Group decision-making
Information Retrieval
Recommender Systems
Consensus models
Fuzzy linguistic modeling
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Conference paper
Published: 19 July 2021 in Lecture Notes in Computer Science
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Nowadays, real world decision making environments are becoming more heterogeneous and flexible than ever. For this reason, we present in this study an innovative multi-criteria group decision making procedure whose main purpose is to be used in scenarios where the decision context is variable. First, the experts can provide their preferences on different moments. Second, by applying a multi-granular linguistic approach, the experts can express themselves more diversely because they select the linguistic label set that they prefer. Third, criteria, alternatives and experts in the course of the decision process. In addition, the procedure makes use of consensus measures to verify that the experts agree with the decision taken.

ACS Style

José Ramón Trillo; Enrique Herrera-Viedma; Francisco Javier Cabrerizo; Juan Antonio Morente-Molinera. A Multi-criteria Group Decision Making Procedure Based on a Multi-granular Linguistic Approach for Changeable Scenarios. Lecture Notes in Computer Science 2021, 284 -295.

AMA Style

José Ramón Trillo, Enrique Herrera-Viedma, Francisco Javier Cabrerizo, Juan Antonio Morente-Molinera. A Multi-criteria Group Decision Making Procedure Based on a Multi-granular Linguistic Approach for Changeable Scenarios. Lecture Notes in Computer Science. 2021; ():284-295.

Chicago/Turabian Style

José Ramón Trillo; Enrique Herrera-Viedma; Francisco Javier Cabrerizo; Juan Antonio Morente-Molinera. 2021. "A Multi-criteria Group Decision Making Procedure Based on a Multi-granular Linguistic Approach for Changeable Scenarios." Lecture Notes in Computer Science , no. : 284-295.

Article
Published: 07 July 2021 in Machine Learning
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The purpose of this paper is to introduce a new distance metric learning algorithm for ordinal regression. Ordinal regression addresses the problem of predicting classes for which there is a natural ordering, but the real distances between classes are unknown. Since ordinal regression walks a fine line between standard regression and classification, it is a common pitfall to either apply a regression-like numerical treatment of variables or underrate the ordinal information applying nominal classification techniques. On a different note, distance metric learning is a discipline that has proven to be very useful when improving distance-based algorithms such as the nearest neighbors classifier. In addition, an appropriate distance can enhance the explainability of this model. In our study we propose an ordinal approach to learning a distance, called chain maximizing ordinal metric learning. It is based on the maximization of ordered sequences in local neighborhoods of the data. This approach takes into account all the ordinal information in the data without making use of any of the two extremes of classification or regression, and it is able to adapt to data for which the class separations are not clear. We also show how to extend the algorithm to learn in a non-linear setup using kernel functions. We have tested our algorithm on several ordinal regression problems, showing a high performance under the usual evaluation metrics in this domain. Results are verified through Bayesian non-parametric testing. Finally, we explore the capabilities of our algorithm in terms of explainability using the case-based reasoning approach. We show these capabilities empirically on two different datasets, experiencing significant improvements over the case-based reasoning with the traditional Euclidean nearest neighbors.

ACS Style

Juan Luis Suárez; Salvador García; Francisco Herrera. Ordinal regression with explainable distance metric learning based on ordered sequences. Machine Learning 2021, 1 -34.

AMA Style

Juan Luis Suárez, Salvador García, Francisco Herrera. Ordinal regression with explainable distance metric learning based on ordered sequences. Machine Learning. 2021; ():1-34.

Chicago/Turabian Style

Juan Luis Suárez; Salvador García; Francisco Herrera. 2021. "Ordinal regression with explainable distance metric learning based on ordered sequences." Machine Learning , no. : 1-34.

Journal article
Published: 06 July 2021 in Applied Soft Computing
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Owing to the rapidly changing customer preferences and demands, the e-commerce industry encounters various uncertainties and risks to enhance competitiveness. Quality function deployment (QFD) is a commonly used model, which can translate customer requirements (CRs) into products or service design requirements (DRs), to improve competitiveness by launching a new business. To measure the uncertainties and behavioral risk factors in e-commerce service design, we propose a new QFD model based on the Kano model and TOPSIS method by considering the behavior of experts with prospect theory under interval type-2 fuzzy linguistic environment. The categories of CRs are identified using the Kano model and the weights of CRs are determined dynamically according to the development stages of enterprises. The priorities of DRs are ranked using the extended TOPSIS method with prospect theory. A case study of China’s e-commerce service design is used to show the application of the proposed QFD model. The prioritizing results show the flexibility of the proposed model on determining the weights of CRs and the priorities of DRs for enterprises at different development stages.

ACS Style

Tong Wu; Xinwang Liu; Jindong Qin; Francisco Herrera. An interval type-2 fuzzy Kano-prospect-TOPSIS based QFD model: Application to Chinese e-commerce service design. Applied Soft Computing 2021, 111, 107665 .

AMA Style

Tong Wu, Xinwang Liu, Jindong Qin, Francisco Herrera. An interval type-2 fuzzy Kano-prospect-TOPSIS based QFD model: Application to Chinese e-commerce service design. Applied Soft Computing. 2021; 111 ():107665.

Chicago/Turabian Style

Tong Wu; Xinwang Liu; Jindong Qin; Francisco Herrera. 2021. "An interval type-2 fuzzy Kano-prospect-TOPSIS based QFD model: Application to Chinese e-commerce service design." Applied Soft Computing 111, no. : 107665.

Journal article
Published: 30 June 2021 in IEEE Transactions on Cybernetics
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Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided.

ACS Style

Cong-Cong Li; Haiming Liang; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making. IEEE Transactions on Cybernetics 2021, PP, 1 -12.

AMA Style

Cong-Cong Li, Haiming Liang, Yucheng Dong, Francisco Chiclana, Enrique Herrera-Viedma. Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making. IEEE Transactions on Cybernetics. 2021; PP (99):1-12.

Chicago/Turabian Style

Cong-Cong Li; Haiming Liang; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 22 June 2021 in IEEE Internet of Things Journal
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The Internet of Things (IoT) enables the interconnection of new cyber–physical devices that generate significant traffic of distributed, heterogeneous, and dynamic data at the network edge. Since several IoT applications demand for short response times (e.g., industrial applications, emergency management, real-time systems, and healthcare systems) and, at the same time, rely on resource-constrained devices, the adoption of traditional data mining techniques is neither effective nor efficient. Therefore, conventional data mining techniques need to be adjusted for optimizing response times, energy consumption, and data traffic while still providing adequate accuracy as required by the IoT applications.

ACS Style

Giancarlo Fortino; Rajkumar Buyya; Min Chen; Francisco Herrera. Special Issue on Methods and Infrastructures for Data Mining at the Edge of Internet of Things. IEEE Internet of Things Journal 2021, 8, 10220 -10221.

AMA Style

Giancarlo Fortino, Rajkumar Buyya, Min Chen, Francisco Herrera. Special Issue on Methods and Infrastructures for Data Mining at the Edge of Internet of Things. IEEE Internet of Things Journal. 2021; 8 (13):10220-10221.

Chicago/Turabian Style

Giancarlo Fortino; Rajkumar Buyya; Min Chen; Francisco Herrera. 2021. "Special Issue on Methods and Infrastructures for Data Mining at the Edge of Internet of Things." IEEE Internet of Things Journal 8, no. 13: 10220-10221.

Journal article
Published: 16 June 2021 in IEEE Transactions on Cybernetics
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In linguistic decision-making problems, there may be cases 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, that is, decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this article, 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.

ACS Style

Cong-Cong Li; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics. IEEE Transactions on Cybernetics 2021, PP, 1 -11.

AMA Style

Cong-Cong Li, Yucheng Dong, Francisco Chiclana, Enrique Herrera-Viedma. Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics. IEEE Transactions on Cybernetics. 2021; PP (99):1-11.

Chicago/Turabian Style

Cong-Cong Li; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics." IEEE Transactions on Cybernetics PP, no. 99: 1-11.

Journal article
Published: 15 June 2021 in Mathematics
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Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

ACS Style

Julia García Cabello; Pedro Castillo; Maria-Del-Carmen Aguilar-Luzon; Francisco Chiclana; Enrique Herrera-Viedma. A Methodology for Redesigning Networks by Using Markov Random Fields. Mathematics 2021, 9, 1389 .

AMA Style

Julia García Cabello, Pedro Castillo, Maria-Del-Carmen Aguilar-Luzon, Francisco Chiclana, Enrique Herrera-Viedma. A Methodology for Redesigning Networks by Using Markov Random Fields. Mathematics. 2021; 9 (12):1389.

Chicago/Turabian Style

Julia García Cabello; Pedro Castillo; Maria-Del-Carmen Aguilar-Luzon; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "A Methodology for Redesigning Networks by Using Markov Random Fields." Mathematics 9, no. 12: 1389.

Journal article
Published: 14 June 2021 in IEEE Access
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Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates’ order to be compared, fingerprint indexing algorithms that reduce the search space while minimizing the increase in the error rate (compared to the identification) are developed. In the present research, we propose an algorithm for indexing latent fingerprints based on minutia cylinder codes (MCC). This type of minutiae descriptor presents a fixed structure, which brings advantages in terms of efficiency. Besides, in recent studies, this descriptor has shown an identification error rate, at the local level, lower than the other descriptors reported in the literature. Our indexing proposal requires an initial step to construct the indices, in which it uses k-means++ clustering algorithm to create groups of similar minutia cylinder codes corresponding to the impressions of a set of databases. K-means++ allows for a better outcome over other clustering algorithms because of the selection of the proper centroids. The buckets associated with each index are populated with the background databases. Then, given a latent fingerprint, the algorithm extracts the minutia cylinder codes associated with the clusters’ indices with the lowest distance respect to each descriptor of this latent fingerprint. Finally, it integrates the votes represented by the fingerprints obtained to select the candidate impressions. We conduct a set of experiments in which our proposal outperforms current rival algorithms in presence of different databases and descriptors. Also, the primary experiment reduces the search space by four orders of magnitude when the background database contains more than one million impressions.

ACS Style

Ismay Perez-Sanchez; Barbara Cervantes; Miguel Angel Medina-Perez; Raul Monroy; Octavio Loyola-Gonzalez; Salvador Garcia; Francisco Herrera. An indexing algorithm based on clustering of minutia cylinder codes for fast latent fingerprint identification. IEEE Access 2021, 9, 1 -1.

AMA Style

Ismay Perez-Sanchez, Barbara Cervantes, Miguel Angel Medina-Perez, Raul Monroy, Octavio Loyola-Gonzalez, Salvador Garcia, Francisco Herrera. An indexing algorithm based on clustering of minutia cylinder codes for fast latent fingerprint identification. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Ismay Perez-Sanchez; Barbara Cervantes; Miguel Angel Medina-Perez; Raul Monroy; Octavio Loyola-Gonzalez; Salvador Garcia; Francisco Herrera. 2021. "An indexing algorithm based on clustering of minutia cylinder codes for fast latent fingerprint identification." IEEE Access 9, no. : 1-1.

Journal article
Published: 06 June 2021 in Information Sciences
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Recent multi-criteria group decision making methods focus their analysis on the experts preferences. They do not take into account the reasons why each expert has provided a specific set of preferences. In this paper, a method that introduces novel measures capable of explaining the reasons behind experts decisions is presented. A novel concept, the arguments are presented. They represent the experts have for maintaining a certain position in the debate. Several measures related to the arguments are proposed. These new argumentation measures, along with consensus measures, help us to get a clear idea about how and why a specific resolution has been reached. They help us to determine which is the most influential expert, that is, the expert whose contributions to the debate have inspired the rest. Also, the proposed method allows us to determine which are the arguments that most of the experts have followed. A clear overview about how the debate is evolving in terms of arguments is also provided. The novel presented analysis indicate how the experts change their opinions in every round and what was the reason for it, which changes have occurred between rounds and they also provide global analysis results.

ACS Style

J.A. Morente-Molinera; G. Kou; K. Samuylov; F.J. Cabrerizo; E. Herrera-Viedma. Using argumentation in expert’s debate to analyze multi-criteria group decision making method results. Information Sciences 2021, 573, 433 -452.

AMA Style

J.A. Morente-Molinera, G. Kou, K. Samuylov, F.J. Cabrerizo, E. Herrera-Viedma. Using argumentation in expert’s debate to analyze multi-criteria group decision making method results. Information Sciences. 2021; 573 ():433-452.

Chicago/Turabian Style

J.A. Morente-Molinera; G. Kou; K. Samuylov; F.J. Cabrerizo; E. Herrera-Viedma. 2021. "Using argumentation in expert’s debate to analyze multi-criteria group decision making method results." Information Sciences 573, no. : 433-452.

Journal article
Published: 01 June 2021 in Information Fusion
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This article proposes a bidirectional feedback mechanism for consensus in group decision making (GDM) driven by the behavior of decision makers (DMs), which is discriminated with a flexible harmony degree as one of three possible states: (1) ‘tolerance behavior’; (2) ‘rationalist behavior’; and (3) ‘conflict behavior’. The first two states are possible to be resolved in the consensus reaching process with one round of feedback recommendations to the discordant DMs. However, in the conflict state, which implies the lack of harmony between the group aim of ‘consensus’ and the individual benefit, it is unreasonable to be resolved with only discordant DMs’ feedback recommendations, and concordant DMs are also expected to make concessions at some degree. To address this not so unusual research problem, a theoretical bidirectional feedback mechanism framework for consensus is developed. Firstly, a maximum consensus driven feedback model is proposed to resolve ‘conflict behavior’ between the concordant and discordant DMs. Secondly, a maximum harmony driven feedback model is activated to support the discordant DMs to reach the threshold values of group consensus. A numerical example is provided to illustrate and verify the proposed mechanism usefulness and how it compares against other existent feedback mechanisms in terms of the extent up to which DMs’ preferences are changed for reaching consensus.

ACS Style

Mingshuo Cao; Jian Wu; Francisco Chiclana; Enrique Herrera-Viedma. A bidirectional feedback mechanism for balancing group consensus and individual harmony in group decision making. Information Fusion 2021, 76, 133 -144.

AMA Style

Mingshuo Cao, Jian Wu, Francisco Chiclana, Enrique Herrera-Viedma. A bidirectional feedback mechanism for balancing group consensus and individual harmony in group decision making. Information Fusion. 2021; 76 ():133-144.

Chicago/Turabian Style

Mingshuo Cao; Jian Wu; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "A bidirectional feedback mechanism for balancing group consensus and individual harmony in group decision making." Information Fusion 76, no. : 133-144.

Journal article
Published: 01 June 2021 in European Journal of Operational Research
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Preference aggregation in Group Decision Making (GDM) is a substantial problem that has received a lot of research attention. Decision problems involving fuzzy preference relations constitute an important class within GDM. Legacy approaches dealing with the latter type of problems can be classified into indirect approaches, which involve deriving a group preference matrix as an intermediate step, and direct approaches, which deduce a group preference ranking based on individual preference rankings. Although the work on indirect approaches has been extensive in the literature, there is still a scarcity of research dealing with the direct approaches. In this paper we present a direct approach towards aggregating several fuzzy preference relations on a set of alternatives into a single weighted ranking of the alternatives. By mapping the pairwise preferences into transitions probabilities, we are able to derive a preference ranking from the stationary distribution of a stochastic matrix. Interestingly, the ranking of the alternatives obtained with our method corresponds to the optimizer of the Maximum Likelihood Estimation of a particular Bradley-Terry-Luce model. Furthermore, we perform a theoretical sensitivity analysis of the proposed method supported by experimental results and illustrate our approach towards GDM with a concrete numerical example. This work opens avenues for solving GDM problems using elements of probability theory, and thus, provides a sound theoretical fundament as well as plausible statistical interpretation for the aggregation of expert opinions in GDM.

ACS Style

Anis Yazidi; Magdalena Ivanovska; Fabio M. Zennaro; Pedro G. Lind; Enrique Herrera Viedma. A new decision making model based on Rank Centrality for GDM with fuzzy preference relations. European Journal of Operational Research 2021, 1 .

AMA Style

Anis Yazidi, Magdalena Ivanovska, Fabio M. Zennaro, Pedro G. Lind, Enrique Herrera Viedma. A new decision making model based on Rank Centrality for GDM with fuzzy preference relations. European Journal of Operational Research. 2021; ():1.

Chicago/Turabian Style

Anis Yazidi; Magdalena Ivanovska; Fabio M. Zennaro; Pedro G. Lind; Enrique Herrera Viedma. 2021. "A new decision making model based on Rank Centrality for GDM with fuzzy preference relations." European Journal of Operational Research , no. : 1.

Journal article
Published: 27 May 2021 in Sustainability
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The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

ACS Style

Sergio Alonso; Rosana Montes; Daniel Molina; Iván Palomares; Eugenio Martínez-Cámara; Manuel Chiachio; Juan Chiachio; Francisco Melero; Pablo García-Moral; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability 2021, 13, 6038 .

AMA Style

Sergio Alonso, Rosana Montes, Daniel Molina, Iván Palomares, Eugenio Martínez-Cámara, Manuel Chiachio, Juan Chiachio, Francisco Melero, Pablo García-Moral, Bárbara Fernández, Cristina Moral, Rosario Marchena, Javier Pérez de Vargas, Francisco Herrera. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability. 2021; 13 (11):6038.

Chicago/Turabian Style

Sergio Alonso; Rosana Montes; Daniel Molina; Iván Palomares; Eugenio Martínez-Cámara; Manuel Chiachio; Juan Chiachio; Francisco Melero; Pablo García-Moral; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera. 2021. "Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys." Sustainability 13, no. 11: 6038.

Journal article
Published: 18 May 2021 in IEEE Transactions on Cybernetics
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A two-fold personalized feedback mechanism is established for consensus reaching in social network group decision-making (SN-GDM). It consists of two stages: 1) generating the trusted recommendation advice for individuals and 2) producing a a personalized adoption coefficient for reducing unnecessary adjustment costs. A uninorm interval-valued trust propagation operator is developed to obtain an indirect trust relationship, which is used to generate personalized recommendation advice based on the principle of ``a recommendation being more acceptable the higher the level of trust it derives from.'' An optimization model is built to minimize the total adjustment cost of reaching consensus by determining the personalized feedback adoption coefficient based on individuals' consensus levels. Consequently, the proposed two-fold personalized feedback mechanism achieves a balance between group consensus and individual personality. An example to demonstrate how the proposed two-fold personalized feedback mechanism works is included, which is also used to show its rationality by comparing it with the traditional feedback mechanism in group decision making (GDM).

ACS Style

Jian Wu; Sha Wang; Francisco Chiclana; Enrique Herrera-Viedma. Two-Fold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation. IEEE Transactions on Cybernetics 2021, PP, 1 -12.

AMA Style

Jian Wu, Sha Wang, Francisco Chiclana, Enrique Herrera-Viedma. Two-Fold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation. IEEE Transactions on Cybernetics. 2021; PP (99):1-12.

Chicago/Turabian Style

Jian Wu; Sha Wang; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "Two-Fold Personalized Feedback Mechanism for Social Network Consensus by Uninorm Interval Trust Propagation." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 27 April 2021 in Brain Sciences
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Neuromarketing, consumer neuroscience and neuroaesthetics are a broad research area of neuroscience with an extensive background in scientific publications. Thus, the present study aims to identify the highly cited papers (HCPs) in this research field, to deliver a summary of the academic work produced during the last decade in this area, and to show patterns, features, and trends that define the past, present, and future of this specific area of knowledge. The HCPs show a perspective of those documents that, historically, have attracted great interest from a research community and that could be considered as the basis of the research field. In this study, we retrieved 907 documents and analyzed, through H-Classics methodology, 50 HCPs identified in the Web of Science (WoS) during the period 2010–2019. The H-Classic approach offers an objective method to identify core knowledge in neuroscience disciplines such as neuromarketing, consumer neuroscience, and neuroaesthetics. To accomplish this study, we used Bibliometrix R Package and SciMAT software. This analysis provides results that give us a useful insight into the development of this field of research, revealing those scientific actors who have made the greatest contribution to its development: authors, institutions, sources, countries as well as documents and references.

ACS Style

Pablo Sánchez-Núñez; Manuel Cobo; Gustavo Vaccaro; José Peláez; Enrique Herrera-Viedma. Citation Classics in Consumer Neuroscience, Neuromarketing and Neuroaesthetics: Identification and Conceptual Analysis. Brain Sciences 2021, 11, 548 .

AMA Style

Pablo Sánchez-Núñez, Manuel Cobo, Gustavo Vaccaro, José Peláez, Enrique Herrera-Viedma. Citation Classics in Consumer Neuroscience, Neuromarketing and Neuroaesthetics: Identification and Conceptual Analysis. Brain Sciences. 2021; 11 (5):548.

Chicago/Turabian Style

Pablo Sánchez-Núñez; Manuel Cobo; Gustavo Vaccaro; José Peláez; Enrique Herrera-Viedma. 2021. "Citation Classics in Consumer Neuroscience, Neuromarketing and Neuroaesthetics: Identification and Conceptual Analysis." Brain Sciences 11, no. 5: 548.

Journal article
Published: 02 April 2021 in Symmetry
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Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

ACS Style

Krishna Kumar Sharma; Ayan Seal; Enrique Herrera-Viedma; Ondrej Krejcar. An Enhanced Spectral Clustering Algorithm with S-Distance. Symmetry 2021, 13, 596 .

AMA Style

Krishna Kumar Sharma, Ayan Seal, Enrique Herrera-Viedma, Ondrej Krejcar. An Enhanced Spectral Clustering Algorithm with S-Distance. Symmetry. 2021; 13 (4):596.

Chicago/Turabian Style

Krishna Kumar Sharma; Ayan Seal; Enrique Herrera-Viedma; Ondrej Krejcar. 2021. "An Enhanced Spectral Clustering Algorithm with S-Distance." Symmetry 13, no. 4: 596.

Journal article
Published: 01 April 2021 in Applied Soft Computing
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Performance of Machine Learning models heavily depends on the quality of the training dataset. Among others, the quality of training data relies on the consistency of the labels assigned to similar items. Indeed, the labels should be coherently assigned (or collected) by avoiding inconsistencies for increasing the performance of the machine learning model. This study focuses on evaluating training data consistency for machine learning algorithms dealing with ranking problems, i.e., the Learning to Rank methods (LTR). This work defines a training data consistency measure based on the consensus value introduced in Group Decision Making. It investigates the statistical relationship between the proposed consistency measure and the performance of a deep neural network implementing an LTR method. This measure could drive data filtering at the training stage and guide model update decisions. Experimentation reveals a strong correlation between the proposed consistency measure and the performance of the model.

ACS Style

Giuseppe Fenza; Mariacristina Gallo; Vincenzo Loia; Francesco Orciuoli; Enrique Herrera-Viedma. Data set quality in Machine Learning: Consistency measure based on Group Decision Making. Applied Soft Computing 2021, 106, 107366 .

AMA Style

Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Francesco Orciuoli, Enrique Herrera-Viedma. Data set quality in Machine Learning: Consistency measure based on Group Decision Making. Applied Soft Computing. 2021; 106 ():107366.

Chicago/Turabian Style

Giuseppe Fenza; Mariacristina Gallo; Vincenzo Loia; Francesco Orciuoli; Enrique Herrera-Viedma. 2021. "Data set quality in Machine Learning: Consistency measure based on Group Decision Making." Applied Soft Computing 106, no. : 107366.

Methodologies and application
Published: 17 March 2021 in Soft Computing
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Due to the importance of correlation coefficient in data analysis, researchers have shown interest in the concept of correlation coefficient for the extensions of fuzzy sets, in particular, for a recent established extension known as hesitant fuzzy set (HFS). Most of the existing correlation coefficients for fuzzy sets return the value one to reflect a perfect linear relationship between objects, but they do not give us sufficient information about the scale with that the objects are correlated. This can be regarded as a disadvantage from the decision-making viewpoint. Moreover, some of the existing correlation coefficients were undefined in the case that the two objects are the same. To overcome such drawbacks, we propose an approach for deriving the modified correlation coefficients of HFSs based on hesitancy degree and then extend the approach to that of interval-valued hesitant fuzzy sets. Furthermore, we put forward a number of new correlation coefficients for HFSs based on Jaccard’s and Dice’s similarity criteria. Then, we give a practical example to illustrate the application of the improved correlation coefficients for HFSs in medical diagnosis. However, comparing the results of the proposed correlation coefficients with those of the other existing definitions shows that the diagnosis results may be quite different, and this is due to their orientation.

ACS Style

B. Farhadinia; H. Liao; E. Herrera-Viedma. A modified class of correlation coefficients of hesitant fuzzy information. Soft Computing 2021, 25, 7009 -7028.

AMA Style

B. Farhadinia, H. Liao, E. Herrera-Viedma. A modified class of correlation coefficients of hesitant fuzzy information. Soft Computing. 2021; 25 (10):7009-7028.

Chicago/Turabian Style

B. Farhadinia; H. Liao; E. Herrera-Viedma. 2021. "A modified class of correlation coefficients of hesitant fuzzy information." Soft Computing 25, no. 10: 7009-7028.

Journal article
Published: 16 March 2021 in Applied Soft Computing
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The linear uncertain preference relations (LUPRs) uses an uncertain variable to represent the decision-maker’s pairwise comparison judgment of the scheme. The variable is subject to the linear uncertain distribution, which is the extension of the traditional fuzzy preference relations (FPRs) and interval fuzzy preference relations (IFPRs). This paper proposes a group decision modeling problem, constructs the priority weights acquisition models of the additively and multiplicatively consistent LUPRs, and especially solves the problem that the weight solution is negative value or no solution by using traditional methods to acquire weights in consistent FPRs and IFPRs. Based on these two types of consistent structure of LUPRs, this study constructs the crisp number, interval number weight solving models of LUPRs and the group decision ranking models with LUPRs. The results show that these new models are suitable for solving the weight vector of traditional FPRs and IFPRs. The case of online shopping platform selection compares the results obtained by various methods of calculating weights, further illustrating the effectiveness and rationality of the new methods.

ACS Style

Weiwei Guo; Zaiwu Gong; Enrique Herrera-Viedma; Qingsheng Li. Priority weights acquisition of linear uncertain preference relations and its application in the ranking of online shopping platforms. Applied Soft Computing 2021, 105, 107292 .

AMA Style

Weiwei Guo, Zaiwu Gong, Enrique Herrera-Viedma, Qingsheng Li. Priority weights acquisition of linear uncertain preference relations and its application in the ranking of online shopping platforms. Applied Soft Computing. 2021; 105 ():107292.

Chicago/Turabian Style

Weiwei Guo; Zaiwu Gong; Enrique Herrera-Viedma; Qingsheng Li. 2021. "Priority weights acquisition of linear uncertain preference relations and its application in the ranking of online shopping platforms." Applied Soft Computing 105, no. : 107292.

Journal article
Published: 13 March 2021 in Building and Environment
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The term “smart city” has been emerged as a novel solution to uphold the useless urban areas and the term has taken the advantage of sustainable and environmental resources. On the other hand, the term “floating city” has been studied for just only a few years as alternative living spaces for humanity across the world since land scarcity has already begun. Therefore, in this research, we propose multi-objective optimization algorithms to obtain the Pareto front solutions for the cuboid open traveling salesman problem (COTSP) in a “smart floating city” context. Given n nodes and the distances between each pair of nodes, the COTSP in this paper aims to find the shortest possible tour with a traveling distance that starts from the depot (i.e., node 1) and visits each node exactly once without needing to return to the depot. As known, a cuboid has height, length, and depth and the COTSP defines its x, y, z coordinates as a cuboid corresponding to height, length, and depth. In addition to the traveling distance, the platform (building breakwaters) cost is measured by the z coordinates (depths) of the nodes/platforms that represent both the platforms below the sea level. Note that unlike the traditional TSP, it has a variable seed number and a variable number of nodes/platforms in each solution. The paper aims to find the Pareto front solutions by minimizing the traveling distance and platform cost of the infrastructures below the sea level simultaneously. We develop a multi-objective self-adaptive differential evolution (MOJDE) algorithm, a nondominated sorting genetic algorithm (NSGAII), and a harmony search (MOHS) algorithm to solve the problem in such a way that we minimize the traveling distance while minimizing the platform cost simultaneously. All algorithms are compared to each other. The computational results show that the MOJDE and NSGAII algorithms outperform the MOHS algorithm in terms of commonly used performance measures from the literature.

ACS Style

Ayca Kirimtat; Ondrej Krejcar; M. Fatih Tasgetiren; Enrique Herrera-Viedma. Multi-performance based computational model for the cuboid open traveling salesman problem in a smart floating city. Building and Environment 2021, 196, 107721 .

AMA Style

Ayca Kirimtat, Ondrej Krejcar, M. Fatih Tasgetiren, Enrique Herrera-Viedma. Multi-performance based computational model for the cuboid open traveling salesman problem in a smart floating city. Building and Environment. 2021; 196 ():107721.

Chicago/Turabian Style

Ayca Kirimtat; Ondrej Krejcar; M. Fatih Tasgetiren; Enrique Herrera-Viedma. 2021. "Multi-performance based computational model for the cuboid open traveling salesman problem in a smart floating city." Building and Environment 196, no. : 107721.

Journal article
Published: 01 March 2021 in IEEE Transactions on Fuzzy Systems
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Multi-criteria group decision making environments that have a high number of criteria values can be difficult for the experts to handle. This is due to the fact that the experts have to take too much information into account. Thus, they get lost among all the possibilities and have difficulties making the right decision. In order to solve this problem we present a novel multi-criteria group decision making method that reduces the initial set of criteria values in an organized way. Hierarchical clustering methods are used in order to generate a new reduced criteria set that can be handled by the experts. Fuzzy ontologies are used as an aid system that stores how much each alternative fulfills each criterion. The presented method makes it possible for the experts to carry out the group decision making process by focusing on ranking the reduced set of criteria values. As a result, a comfortable decision environment is generated, in which the experts can make decisions by managing a fair amount of information. The aid provided by fuzzy ontologies allow the experts to focus on establishing the importance of the criteria values, leaving the rest to the computational system.

ACS Style

Juan Antonio Morente-Molinera; Yinglin Wang; Zai-Wu Gong; Ali Morfeq; Rami Al-Hmouz; Enrique Herrera-Viedma. Reducing criteria values in multi-criteria group decision making methods using hierarchical clustering methods and fuzzy ontologies. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Juan Antonio Morente-Molinera, Yinglin Wang, Zai-Wu Gong, Ali Morfeq, Rami Al-Hmouz, Enrique Herrera-Viedma. Reducing criteria values in multi-criteria group decision making methods using hierarchical clustering methods and fuzzy ontologies. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Juan Antonio Morente-Molinera; Yinglin Wang; Zai-Wu Gong; Ali Morfeq; Rami Al-Hmouz; Enrique Herrera-Viedma. 2021. "Reducing criteria values in multi-criteria group decision making methods using hierarchical clustering methods and fuzzy ontologies." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.