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21st International conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9781643683164

Fuzzy Decision Making is a line of research that has had an important presence in the literature since 1965 until nowadays. From that year to the present, the line of research has been consolidated. The research field goals have evolved along with all the research that makes use of these systems and the new technologies requirements. Therefore, new challenges, different from the ones that were faced at the beginning, need to be solved. The aim of this paper is to describe the recent challenges faced by those working in this line of research. This paper will help researchers to know how their work should be oriented in the future. © 2022 The authors and IOS Press. All rights reserved.

关键词： Decision making

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A Group Decision-Making Approach Leveraging Preference Relations Derived from Large Language Model 11

A Group Decision-Making Approach Leveraging Preference Relat...

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11th International conference on Information Technology and Quantitative Management, ITQM 2024

作者： Trillo, José Ramón Martínez, María Ángeles Zadrozny, Slawomir Kacprzyk, Janusz Herrera-Viedma, Enrique cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain Department of Social Work and Social Services University of Granada Granada18001 Spain Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw01-447 Poland Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Group decision-making involves selecting among limited options. Experts share their perspectives in a comparative debate but then evaluate alternatives using preference relations, which can result in inconsistencies between their expressions and assessments. To address this, a method is proposed that automates the generation of these relationships from the debate comments, classifying them into positive and negative using sentiment analysis, namely with the Large Language Model. A new operator is introduced that weights these comments to calculate preference relations. Furthermore, modification of the relationships is allowed if the experts so wish. Moreover, another operator is incorporated that adjusts the weight of each expert according to his or her active participation in the discussion, assigning more weight to those who contribute more comments. Finally, this innovative method promotes coherence and equal participation in group decision-making by employing an innovative sentiment analysis detection system. © 2024 The Authors.

关键词： Modeling languages

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An Algorithm for Belief Rule Induction with Partial Ignorance

SSRN

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SSRN 2024年

作者： Li, Yangxue Pérez, Ignacio Javier cabrerizo, Francisco Javier Morente-Molinera, Juan Antonio Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain

Belief rules are an extension of fuzzy rules that consider belief degrees. They are widely applied due to their traceability, interpretability, and flexibility. However, current belief rules only consider residual support and global ignorance while ignoring partial ignorance. In this paper, we proposed a novel belief rule-based classification system called partial ignorance belief rule induction algorithm (PIBRIA). The consequent part of belief rules takes into account not only the residual support and global ignorance but also partial ignorance of evidence. The belief degrees in the consequent part of each belief rule collectively form a genuine basic probability assignment (BPA). Based on fuzzy unordered rule induction algorithm (FURIA), we propose the learning algorithm for belief rules with partial ignorance, which helps the novel proposed PRIBIA achieve a balance between accuracy and computational complexity. The inference method employs the Dempster's combination rule and Shafer's discounting operation, where the discounting operation can resolve evidence conflicts in the combination rule. The effectiveness and superiority of PIBRIA in handling classification tasks have been validated by comparing it to some state-of-the-art rule-based models. The results of statistical tests show that PIBRIA outperforms all other methods in terms of classification accuracy. Whereas its computational complexity is moderate. © 2024, The Authors. All rights reserved.

关键词： computational complexity

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Improved Deep Neural Network Performance Under Dynamic Programming Mode

SSRN

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SSRN 2023年

作者： cabello, Julia García Department of Applied Mathematics Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Spain

For Deep Neural Networks (DNN), the standard gradient-based algorithms may not be efficient because of the raised computational expense resulting from the increase in the number of layers. This paper offers an alternative to the classic training solutions: an in-depth study to find conditions under which the underlying ANN minimisation problem can be addressed from a Dynamic Programming (DP) perspective. Specifically, we prove that any ANN with monotonic activation is separable when regarded as a parametric function. Particularly, when the ANN is viewed as a network representation of a dynamical system (as a coupled cell network), we also prove that the transmission-of-signal law is separable provided the activation function is a monotone non-decreasing function. This strategy may have a positive impact on the performance of ANNs by improving their learning accuracy, particularly for DNNs. For our purposes, ANNs are also viewed as universal approximators of continuous functions and as abstract compositions of an even number of functions. This broader representation makes it easier to analyse them from many other perspectives (universal approximation issues, inverse problem solving) leading to a general improvement in knowledge on NNs and their performance. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Large-scale group decision-making: A bibliometric study

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Procedia computer science 2024年 242卷 1198-1205页

作者： Juan carlos González-Quesada José Ramón Trillo María Ángeles Martínez Enrique Herrera-Viedma Francisco Javier cabrerizo Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada 18071 Granada Spain Department of Social Work and Social Services University of Granada 18001 Granada Spain

The process of large-scale group decision-making pertains to the facilitation of decisions within expansive assemblies of individuals across a range of settings, spanning from corporate entities to local communities and governmental bodies. This approach is indispensable given the intricate and heterogeneous nature of the opinions, interests, and perspectives that are prevalent in such extensive collectives. In the realm of large-scale group decision-making, a diverse array of methods and strategies come into play. These include proficient facilitation, leveraging technology, employing various voting methods, and engaging in structured deliberation. These techniques collectively serve to streamline the decision-making process and yield equitable and satisfactory results for all involved parties. Given the fact that this topic has become a hot topic in recent years, the objective of this work is to conduct a bibliometric study on large-scale group decision-making to provide an overview of this emerging area within group decision-making. Aspects such as the main contributing journals, relevant authors and affiliated countries, as well as the most cited documents, authors, and keywords, are examined. This analysis will offer a deeper understanding of the current state and trends in large-scale group decision-making, emphasizing its significance and applicability across various fields of study and practice.

关键词： Large-scale group decision Fuzzy decision-making Bibliometrics

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A Survey on Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2023年

作者： Peláez-Vegas, Adrián Mesejo, Pablo Luengo, Julián The Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Semantic segmentation is one of the most challenging tasks in computer vision. However, in many applications, a frequent obstacle is the lack of labeled images, due to the high cost of pixel-level labeling. In this scenario, it makes sense to approach the problem from a semi-supervised point of view, where both labeled and unlabeled images are exploited. In recent years this line of research has gained much interest and many approaches have been published in this direction. Therefore, the main objective of this study is to provide an overview of the current state of the art in semi-supervised semantic segmentation, offering an updated taxonomy of all existing methods to date. This is complemented by an experimentation with a variety of models representing all the categories of the taxonomy on the most widely used becnhmark datasets in the literature, and a final discussion on the results obtained, the challenges and the most promising lines of future research. copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Teranga Go!: carpooling collaborative consumption community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust

arXiv

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arXiv 2024年

作者： Montes, Rosana Morales, Jeovani Zermeño, Noe Duran, Jeronimo Herrera, Francisco Department of Software Engineering University of Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI Spain

classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution. © 2024, cc BY-Nc-ND.

关键词： Semantics

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copula-Based Estimation of continuous Sources for a class of constrained Rate-Distortion Functions

Copula-Based Estimation of Continuous Sources for a Class of...

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IEEE International Symposium on Information Theory

作者： Giuseppe Serra Photios A. Stavrou Marios Kountouris Communication Systems Department EURECOM Sophia-Antipolis France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We present a new method to estimate the rate-distortion-perception function in the perfect realism regime (PR-RDPF), for multivariate continuous sources subject to a single-letter average distortion constraint. The proposed approach is not only able to solve the specific problem but also two related problems: the entropic optimal transport (EOT) and the output-constrained rate-distortion function (Oc-RDF), of which the PR-RDPF represents a special case. Using copula distributions, we show that the Oc-RDF can be cast as an $I$ -projection problem on a convex set, based on which we develop a parametric solution of the optimal projection proving that its parameters can be estimated, up to an arbitrary precision, via the solution of a convex program. Subsequently, we propose an iterative scheme via gradient methods to estimate the convex program. Lastly, we characterize a Shannon lower bound (SLB) for the PR-RDPF under a mean squared error (MSE) distortion constraint. We support our theoretical findings with numerical examples by assessing the estimation performance of our iterative scheme using the PR-RDPF with the obtained SLB for various sources.

关键词： Gradient methods Rate-distortion Estimation Distortion Iterative methods

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computation of the Multivariate Gaussian Rate-Distortion-Perception Function

Computation of the Multivariate Gaussian Rate-Distortion-Per...

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IEEE International Symposium on Information Theory

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we propose a generic method for computing the rate-distortion-perception function (RDPF) of a multivariate Gaussian source under tensorizable distortion and perception metrics. Through the assumption of a jointly Gaussian reconstruction, we establish that the optimal solution of the RDPF belongs to the vector space spanned by the eigenvector of the source covariance matrix. consequently, the multivariate optimization problem can be expressed as a function of the scalar Gaussian RDPFs of the source marginals, constrained by global distortion and perception levels. Utilizing this result, we devise an alternating minimization scheme based on the block nonlinear Gauss-Seidel method. This scheme solves optimally the optimization problem while identifying the optimal stage-wise distortion and perception levels. Furthermore, the associated algorithmic embodiment is provided, along with the convergence and the rate of convergence characterization. Lastly, in the regime of “perfect realism”, we provide the analytical solution for the multivariate Gaussian RDPF. We corroborate our findings with numerical simulations and draw connections to existing results.

关键词： Measurement Nonlinear distortion Numerical simulation Minimization Vectors covariance matrices Optimization

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Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability

Defense Strategy against Byzantine Attacks in Federated Mach...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE)

作者： Nuria Rodríguez-Barroso Javier Del Ser M. Victoria Luzón Francisco Herrera Dept. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain TECNALIA Basque Research and Technology Alliance (BRTA) Derio Bizkaia Spain University of the Basque Country (UPV/EHU) Bilbao Bizkaia Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

The rise of high-risk AI systems has led to escalating concerns, prompting regulatory efforts such as the recently approved EU AI Act. In this context, the development of responsible AI systems is crucial. To this end, trustworthy AI techniques aim at requirements (including transparency, privacy awareness and fairness) that contribute to the development of responsible, robust and safe AI systems. Among them, Federated Learning (FL) has emerged as a key approach to safeguarding data privacy while enabling the collaborative training of AI models. However, FL is prone to adversarial attacks, particularly byzantine attacks, which aim to modify the behavior of the model. This work addresses this issue by proposing an eXplainable and Impartial Dynamic Defense against Byzantine Attacks (XI-DDaBA). This defense mechanism relies on robust aggregation operators and filtering techniques to mitigate the effects of adversarial attacks in FL, while providing explanations for its decisions and ensuring that clients with poor data quality are not discriminated. Experimental simulations are discussed to assess the performance of XI-DDaBA against other baselines from the literature, and to showcase its provided explanations. Overall, XI-DDaBA aligns with the need for responsible AI systems in high-risk collaborative learning scenarios through the explainable and impartial provision of robustness against attacks.

关键词： Fuzzy logic Training Privacy data privacy Federated learning Filtering Robustness

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