http://hdl.handle.net/10366/68585 Thu, 30 Apr 2026 20:33:06 GMT 2026-04-30T20:33:06Z http://hdl.handle.net/10366/171197 [EN]Multi-source information fusion has become the cornerstone of modern intelligent decision systems, yet the explosive volume, heterogeneity and high dimensionality of crowd-contributed data severely challenge the efficiency and fairness of large-scale group decision-making (LSGDM). To transform this “data richness” into “decision wisdom”, dimensionality-reduction technologies have evolved from optional pre-processing modules to indispensable fusion-centric enablers that compress numerous decision makers into a compact but representative subgroup structure while preserving collective knowledge. By systematically reviewing 345 publications (2014–2025) retrieved from the Web of Science, this paper proposes the first information-fusion-oriented taxonomy of LSGDM dimensionality-reduction techniques, clustering them into: (i) clustering-analysis-based fusion of large-scale preference data, (ii) complex-network-based community detection that fuses relational information, and (iii) specialized hybrid methods that fuse multi-modal or semi-supervised cues. This review critically analyzes the above techniques, discusses their roles in alleviating scale dilemmas, improving decision quality and process operability, and establishes an evaluation system covering internal and external validity. Finally, it summarizes key challenges in information fusion, including dynamic streaming preferences, ultra-large sparse networks, and privacy-preserving fusion, and prospects paradigm optimization and key technologies such as semi-supervised granular ball fusion, autoencoder-based deep representation fusion, ensemble learning, and multimodal fusion, providing references for dimensionality reduction to support efficient, reliable, and fair LSGDM in the big data era. Thu, 01 Oct 2026 00:00:00 GMT http://hdl.handle.net/10366/171197 2026-10-01T00:00:00Z http://hdl.handle.net/10366/170922 [EN]Bustince et al. recently introduced a versatile framework for constructing aggregation functions from inputdependent [0, 1]-valued vectors under appropriate conditions. Here we leverage their framework to propose and investigate a new and structurally different family of operators. Whereas in Choquet-inspired aggregation functions, the weights applied to a difference of successive values depend on the inputs for which they are not weighting, the weights that are applied in the new model remain constant across the inputs that belong to an element of a fixed partition (in the sense of basic set theory) of the set of inputs. Consequently, a key structural distinction is that, in contrast to the former model, the number of weights is inherently finite when the partition is simplicial (a technical concept that aligns with the spirit of Choquet integration). When the partition is the finest (therefore, it is infinite) we obtain the general framework that motivates our investigation, hence the new model generalizes Choquet integrals. We prove that it also encompasses a type of aggregation operators based on weighting vectors that is more general than Ordered Weighted Averaging operators and Induced Ordered Weighted Averaging operators. Other mathematical proofs establish when this new class of idempotent operators has properties such as shift-invariance and continuity. Our proofs identify natural conditions guaranteeing that the new operators are aggregation functions. Numerical examples involving bivariate inputs illustrate their geometric interpretation. This new family of operators highlights the foundational importance and flexibility of the general framework designed by Bustince et al. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/170922 2026-01-01T00:00:00Z http://hdl.handle.net/10366/170258 [ES]Lack of certainty is a major issue in decision-making, data analysis and modeling in many fields. Soft set theory has evolved into an effective method for addressing this issue, both due to its adaptability and its ability to blend with other models for the representation of uncertainty (inclusive of fuzzy sets and its extensions). Membership-based theories alone are incompatible with slackness in the evaluations, such as those that stem from measurement errors. This issue led to the appearance of interval-valued fuzzy sets (with only membership), and afterwards interval-valued intuitionistic fuzzy sets and circular intuitionistic fuzzy sets (with both membership and non-membership), plus other extensions that allow for wider domains of the evaluations. Circular Pythagorean fuzzy sets were defined with this motivation, and here we first combine them with soft set theory. In this setting, we propose novel score and accuracy functions based on optimistic and pessimistic perspectives. This is done with the help of a decision-maker-controlled parameter 𝜆 ∈ [0,1] that captures her/his approach to the problem. Furthermore, inspired by related models we define essential operations for the new framework, including complement, min-OR, max-OR, min-AND, max-AND, min-union, max-union, min-intersection, and max-intersection. All these tools allow us to extend decision-making strategies designed for Pythagorean fuzzy soft sets to the circular domain motivated by considerations of slackness. Finally, the study compares the proposed approach with circular intuitionistic fuzzy soft set based decision-making models to evaluate its effectiveness. Sun, 01 Mar 2026 00:00:00 GMT http://hdl.handle.net/10366/170258 2026-03-01T00:00:00Z http://hdl.handle.net/10366/170257 [EN]This article presents the first self-contained and direct proof of a widely recognized result: that the discrete Choquet integral, when defined from a discrete fuzzy measure (or capacity), is convex if and only if the discrete fuzzy measure itself is submodular. In contrast to existing proofs, our argument is constructed directly from the fuzzy measure defined on a finite set, employing only standard techniques from the theory of capacities and Choquet integration. Mon, 01 Dec 2025 00:00:00 GMT http://hdl.handle.net/10366/170257 2025-12-01T00:00:00Z http://hdl.handle.net/10366/169279 [EN]We study the geometry of real hypersurfaces immersed in e-(J⁴ = 1)-Kaehler manifolds, a class of semi-Riemannian manifolds that generalizes both classical Kaehler and para-Kaehler structures. After recalling the fundamental definitions and properties of e-(J⁴ = 1)-Kaehler manifolds, we derive the induced geometric structure on a real hypersurface, considering both non-degenerate and degenerate (null) cases with respect to the semi-Riemannian metric. We characterize the integrable distributions arising on such hypersurfaces in terms of their second fundamental forms and shape operators, and identify conditions that distinguish CR-type substructures within the hypersurface. Our results extend classical submanifold theory to the broader context of e-(J⁴ = 1)-geometry, highlighting the interplay between the ambient complex-product structure and the intrinsic geometry of hypersurfaces. Sat, 01 Jan 1994 00:00:00 GMT http://hdl.handle.net/10366/169279 1994-01-01T00:00:00Z http://hdl.handle.net/10366/169266 [EN]The objective of this study was to evaluate the performance of a locally derived risk-adjusted model to predict cardiorespiratory morbidity after major lung resection for bronchogenic carcinoma. A logistic regression risk model has been developed using a database of 515 patients undergoing major lung resection between 1994 and 2001. Independent studied variables were: age of the patient, body mass index, predicted postoperative forced expiratory volume in the first second (ppoFEV1%), cardiovascular comorbidity, diabetes mellitus, induction chemotherapy, tumour staging, extent of resection, chest wall resection, and perioperative blood transfusion. The analyzed outcome was the occurrence of postoperative cardiorespiratory complications prospectively recorded and codified. Variables with an influence on the outcome on univariate analysis were entered in the risk model. The calculated probabilities of complication were compared to its actual occurrence in 53 consecutive cases operated on between January and June 2002 and a receiver operating characteristic (ROC) curve was constructed. On logistic regression analysis, age (P , 0:001) and ppoFEV1 (P¼ 0:003) independently correlated with the outcome. The accuracy for morbidity prediction (area under the ROC curve) was 0.55 (95% CI: 0.31–0.78). These data show that this locally derived lung resection risk-adjusted model fails to predict postoperative cardiorespiratory morbidity in individual patients. Wed, 01 Jan 2003 00:00:00 GMT http://hdl.handle.net/10366/169266 2003-01-01T00:00:00Z http://hdl.handle.net/10366/167898 [EN] The advancement of technology and the rapid developments in the field of artificial intelligence have led to a surge in the research on decision-making in uncertain environments. Furthermore, individual decision-making is too simplistic to solve the complex decision-making problems posed by these challenges, leading to study group decision-making (GDM). Particularly, fuzzy social networks (FSNs) and fuzzy preference relations (FPRs) have important applications in GDM. In addition, probabilistic linguistic term sets (PLTSs) have succeeded as a bridge among natural language, fuzzy reasoning, and probability theory. However, the existing research on GDM under PLTSs faces three key challenges: flaws in PLTSs distance measurement, lack of FSN modeling, and the over-simplified feedback mechanism. These challenges severely impede the effectiveness and reliability of consensus reaching process (CRP) in complex decision-making scenarios. Motivated by these facts, this paper designs a three-way group consensus method based on FSNs under probabilistic linguistic preference relations (PLPRs), namely, the TWD-FSN-PLPR method. This method consists of three successive parts. The first part is the design of an improved consistency method based on the properties of PLTSs. Its main purpose is to ensure that the information on evaluations provided by the decision makers (DMs) maintains internal consistency, paving the way for subsequent GDM. The goal of the second part is to compute the DMs’ weights. Their own familiarity with the PLTS’s cross-entropy and self-confidence are used to construct a directed weighted FSN and then produce the weights from a metric based on social influence. The third part is the consensus reaching process, whose efficiency is improved by a combination of three-way decision and minimum cost, and implementing a penalty mechanism for non-cooperative DMs. In addition, the optimal alternative is selected using regret theory. The methodology is applied to a real case and compared with multiple methods to illustrate its rationality and superiority. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/167898 2026-01-01T00:00:00Z http://hdl.handle.net/10366/167714 [EN] In the era of big data, the integration of data from multiple sources to enhance decision-making processes has become a crucial challenge. Traditional information fusion methods often struggle with incomplete multi-scale information systems, resulting in biased conclusions and an inability to fully leverage the potential connections within the data. This study addresses these limitations by proposing a novel consensus model that integrates asynchronous opinion evolution in the DeGroot model with dynamic trust-driven three-way decision. The method employs adaptive multi-scale clustering partitioning and mutual information to uncover potential associations among the original data, introduces an interval-based neighborhood representation and a weighted aggregation approach to more reasonably impute missing values, and determines the optimal scale through the analysis of the information entropy structure curve. Furthermore, considering the asynchronous behaviors and interactive influences among decision-makers in social networks, the proposed model incorporates a differentiated group consensus regulation method to achieve more flexible and robust group decision-making. The experimental results demonstrate the effectiveness of the method proposed in this paper, with significant improvements in key indicators such as the number of modifications and iterations required to reach a consensus, while maintaining a relatively low adjustment cost. Specifically, the number of modifications and the number of iterations achieved by the proposed method are the lowest among the compared methods, indicating its superior performance in consensus-reaching processes. These findings highlight the model’s ability to efficiently handle incomplete information and enhance the rationality and dynamic adaptability of group decision-making, making it a valuable tool for information fusion. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/167714 2026-01-01T00:00:00Z http://hdl.handle.net/10366/167458 [EN] The transition to renewable energy (RE) is essential for sustainable development and energy security, especially in rapidly urbanized areas. This research presents a new decision-making framework designed to identify the most effective renewable energy sources for Shenzhen, China. The framework employs Recursive Feature Elimination (RFE) to condense 14 initial criteria to the 5 most significant factors, thereby improving evaluation efficiency while maintaining analytical accuracy. This method integrates a logarithmic percentage change-driven objective weighting technique (LOPCOW) with evaluation based on relative utility and nonlinear standardization (ERUNS), which is subsequently enhanced through the application of the linear diophantine fuzzy soft-max average (LiDFSMA) operator. This hybrid model addresses uncertainty in multi-criteria decision making (MCDM) by employing advanced weighting and aggregation techniques. Empirical results indicate that solar thermal power is the most effective alternative for Shenzhen, due to its reliable power generation capacity and suitability for areas with direct sunlight. Pseudocode inclusion promotes transparency and facilitates replicability. The proposed method provides a reliable, objective, and flexible instrument for RE planning, significantly influencing the acceleration of sustainable energy adoption in urban environments. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/167458 2026-01-01T00:00:00Z http://hdl.handle.net/10366/166878 [EN] The motivation of this research is a classical problem in financial mathematics, namely, the subjective valuation of rewards earned in future periods. Various additive formulas have been proposed and criticized both theoretically and experimentally. We start from the premise that interaction among the periods affecting this subjective assessment is allowed. Dispensing with the additive form of time discounting leads to a Choquet evaluation defined from a capacity that encapsulates the interactions and at the same time, coincides with standard evaluations at any single time period. This consideration raises the general capacity compliance problem. We formally pose this question and motivated by computational tractability, we investigate the main traits of its 2-additive configuration. Then we apply our conclusions to two specific statements respectively suggested by the exponential and hyperbolic discounted utility formulas. Sun, 31 Aug 2025 00:00:00 GMT http://hdl.handle.net/10366/166878 2025-08-31T00:00:00Z http://hdl.handle.net/10366/165849 [EN] In an intertemporal framework with a finite horizon, we pose the problem of characterizing which discrete Choquet integrals satisfy strong aversion to delayed rewards. This property is arguably a minimal condition for a reasonable evaluation of vectors of rewards received at successive periods of time. We relate this property to other distributional axioms discussed for this framework. Particularly, we show that for monotonic evaluations, delay-aversion is equivalent to consistency with cumulative domination. Then, we define a property (temporal anti-buoyancy) and we prove that it is necessary and sufficient for a capacity to define a strongly delay-averse Choquet integral. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10366/165849 2025-01-01T00:00:00Z http://hdl.handle.net/10366/164938 [EN]Soft set theory builds on the idea of a parameterized family of subsets of a universal set, where for each pertinent characteristic, any specific member of the universe either satisfies it or not. The concept of an N-soft set sharpens this model with the aid of multinary parameterized descriptions; that is, N-soft sets categorize the options in terms of multiple classifications of the characteristics. The aim of this research is fourfold. First, this research focuses on daily-life decision-making problems that involve both positive and negative attributes that can be naturally distributed among classes. Each comparable group of attributes produces an N-soft set, and we can represent all these N-soft sets using separable N-soft sets. We show that this structure facilitates decision-making in the presence of large numbers of attributes. Second, to develop tools that provide a mechanism for the selection of an alternative in this new model, we first develop a complement operator for N-soft sets to uniformize the data, and then, we propose strategies for taking advantage of the qualities of the attributes. Aggregation operators are employed to aggregate the data into a resultant N-soft set, a fuzzy N-soft set, or a hesitant N-soft set. Several algorithmic procedures are proposed to define these methods. Third, we define the novel notion of a multihesitant N-soft set. This loosely defined concept is helpful for representing data with multiple and repetitive entries while avoiding information loss. Finally, we provide solutions to several real-life decision-making problems to illustrate the versatility of our approaches. We apply this theory to construct a new method for ranking countries participating in the Olympic Games. Our motivation is that the existing lexicographic procedure is unable to distinguish among gold, silver, and bronze medals won at sports with very different characteristics. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10366/164938 2025-01-01T00:00:00Z http://hdl.handle.net/10366/163897 [EN] This paper introduces a novel numerical charac- teristic of hesitant fuzzy soft sets called their scored-energy. We introduce this concept as a combination of two very different tools, namely, scores of hesitant fuzzy elements and singular values of a (not- necessarily square) matrix. The latter idea replicates the concept of energy or nuclear norm of a matrix, which has excellent analytical abilities in graph theory and applications in fields such as statistics or signal processing. Natural algorithms establish applications of scored-energies to both clustering and decision-making. Through examples, we demonstrate how these new techniques are efficient and implementable in practice. Comparisons are conducted that prove their validity, reliability, and credibility. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10366/163897 2025-01-01T00:00:00Z http://hdl.handle.net/10366/163896 [EN] This paper defines approbatory social welfare functions. They are the natural counterpart of Arrovian social welfare functions when the expression of preferences is accompanied by a common choice of “approved” alternatives in a consistent manner. First, we count the number of consistent preference-approval profiles (the domain of approbatory social welfare functions). Then we show that pairwise dictatorial behaviors are characterized by unanimity and independence of irrelevant alternatives, a replication of Arrow’s characterization of dictatorships for this restricted version of the preference-approval model defined by Brams and Sanver in 2009. Sat, 01 Mar 2025 00:00:00 GMT http://hdl.handle.net/10366/163896 2025-03-01T00:00:00Z http://hdl.handle.net/10366/159545 [EN] The primary goal of this research article is to apply ELECTRE I, a fundamental multi-criteria group decisionmaking technique, in an 𝑚-polar fuzzy 𝑁-soft environment. This new methodology helps us to pinpoint the best alternative(s) in the presence of multi-polar options with 𝑁-graded qualities. Its basic operational idea entails the comparison between any two alternatives by the assessment of score degrees. Concordance and discordance indices are then calculated to evaluate the alternatives’ superiority and inferiority. We may disqualify the incompetent alternatives using concordance and discordance levels. An 𝑚-polar fuzzy 𝑁-soft dominance matrix can represent the combined effect of concordance and discordance dominance matrices. The steps of this new multi-criteria group decision making technique are summarized in a flowchart. In order to demonstrate its authenticity and applicability, we employ a case study involving the establishment of a rehabilitation facility for drug abusers. A comparison with the 𝑚-polar fuzzy PROMETHEE and 𝑚-polar fuzzy ELECTRE I methodologies establishes its validity. Finally, we conclude our study of the methodology proposed in this paper with a critical analysis of its benefits and drawbacks. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10366/159545 2023-01-01T00:00:00Z http://hdl.handle.net/10366/159499 [EN] Nowadays, attribute reduction has become a significant topic in relation decision systems. Their applications come from different domains of the computer sciences, including machine learning, data mining and pattern recognition, which often involve a large number of attributes in data. Several attribute reduction methods are presented in the literature in order to help solving decision-making problems efficiently. A common characterization for these approaches is still missing, that is, although attribute reduction methods of relation decision systems and fuzzy relation decision systems exist, a common generalization for them is still missing. This study presents a systematic discussion of attribute reduction based on m-polar fuzzy (mF, in short) relation systems and mF relation decision systems, which are respective extensions of fuzzy relation systems and fuzzy relation decision systems. This study provides mathematical results on the attribute reduction algorithms based upon mF relation systems and mF relation decision systems. Both are explained with numerical examples. The resulting algorithms permit to reinterpret the upshots of traditional reduction methods, providing them with larger generality and unification abilities. Afterwards, two real-life applications of the proposed attribute reduction approaches prove their validity and feasibility. Finally, the attribute reduction methods developed here are compared with some existing approaches to show their reliability. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/10366/159499 2022-01-01T00:00:00Z