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The research on decision making driven by product rankings faces challenges due to the rise of extensive positive reviews and the widespread distribution of electronic word of mouth (eWOM) across multiple platforms. There is a limited body of research that examines the impact of platform credibility on the quality of product rankings. Hence, based on the basic uncertain linguistic information (BULI), which enables simultaneous representation of information and its credibility, we investigate the development of a ratings aggregation approach for cross-platform distribution (CPD) with the aim of facilitating decision-making processes, focusing specifically on the aspect of credibility. To begin with, this paper introduces the concept of BULI as a means to effectively represent both product ratings and their corresponding levels of credibility. Subsequently, we proceeded to devise the BULI-based aggregation functions that are well suited for the aggregation of CPD ratings and that can be degraded to the existing operator. In addition, we develop a credibility evaluation index system and credibility calculation model for the platform in order to derive a product BULI matrix consisting of ratings and their corresponding levels of credibility. In this study, we propose two models, namely the feature information-based user weighting model and the BULI distance measure-based technique for order preference by similarity to an ideal solution (BULI-TOPSIS) model, to enhance the product ratings aggregation approach for decision-making purposes. The utilization of the proposed method is exemplified through the case study of passenger car ranking, showcasing its practicality and efficiency. [ABSTRACT FROM AUTHOR]

In an uncertain and complex decision-making environment, limited by the scope of human cognition, traditional utility decision-making has a certain deviation to actual decision-making. The revision of behavioral decision-making (BDM) to traditional rational decision-making theory makes the new model more universal. In light of this point, we reveal a new three-way decision (3WD) model by virtue of prospect theory (PT) on multi-scale information systems (MS-ISs) for persuing multi-attribute decision-making (MADM) problems. By utilizing an expected evaluation, our newly designed value function can not only reflect the relative position of the object but also avoid the drawbacks of the reference point being too subjective. Through the value function, we obtain a more reasonable avail function to replace the loss function in the traditional 3WD model. At the same time, the weighting function of the object in different states can be calculated, by synthesizing avail function and the weighting function under different decision attitudes. The comprehensive prospect value and classification conditions of the object are calculated. Then, through data selected from the UCI database, we verify the effectiveness of the constructed method. Comparative and experimental analyses are also used to illustrate the superiority and stability of our designed method. [ABSTRACT FROM AUTHOR]

With the increasing complexity of real decision-making problems, some experts try to use multi-scale information to express expert opinions in group decision-making problems. Facing the problem of group decision-making with multi-scale information, this paper attempts to explore a multi-scale group decision-making method consisting of two stages to provide theoretical support and methodological basis for establishing a multi-scale decision analysis system. In the first stage, we introduce a newly ranking decision-making approach based on a reflexive fuzzy α-neighborhood operator to deal with single-scale ranking problems with a single expert, which greatly enriches the ranking decision analysis theory. We also use a numerical example and experimental analysis to detect the stability and validity of the method. In the second stage, considering that the decision-making opinions of multiple experts may appear at the same time in the decision-making process, we propose the score labeling approach and the decision fusion approach to obtain the comprehensive decision-making result, which provides a feasible research idea for the comprehensive analysis of group decision-making results. Combining these two stages, a complete multi-scale group decision-making method in a multi-scale environment is described in detail, which can effectively deal with multi-scale group decision-making problems. Moreover, a series of simulation calculations are conducted to test the validity and stability of the proposed group decision-making method. [ABSTRACT FROM AUTHOR]

Linguistic variables are flexible and intuitive attraction for expressing the wording of decision makers. This paper introduces a new type of linguistic fuzzy sets called linguistic dual hesitant fuzzy sets to express the hesitancy of decision makers' qualitative preferences and non-preferences. Considering the application in decision making, linguistic dual hesitant fuzzy preference relations (LDHFPRs) are introduced that permit the decision makers to apply several linguistic variables to indicate a qualitative preferred judgment and a qualitative non-preferred judgment, respectively. To rank objects from LDHFPRs rationally, a consistency concept is first presented. Then, two optimal models are built to judge the consistency of LDHFPRs. When LDHFPRs are inconsistent, an optimal model-based iteration algorithm for obtaining consistent LDHFPRs is offered. Based on consistent linguistic intuitionistic fuzzy preference relations, a method for calculating the weighted linguistic intuitionistic fuzzy priority vector is introduced. In the setting of group decision making (GDM), a consensus measure based on individually weighted consistent reverse complementary linguistic intuitionistic fuzzy preference relations is defined. When the consensus does not satisfy the requirement, a two-step optimal model-based method for increasing the consensus level is offered. Furthermore, an approach for GDM with LDHFPRs is developed. Finally, an illustrative example concerning the evaluation of basic services internet enterprise websites is provided to show the efficiency of the new method. [ABSTRACT FROM AUTHOR]

In group decision-making problems, experts often form multiple subgroups depending upon their backgrounds or attitudes. To explore the effect of different types of subgroups' formation on the final decision and to make the decision process more conforming to human custom, we propose a comprehensive study on the effect of "multi-subgroup background" on a basis of weights optimization and Granular Computing techniques. Firstly, a comparison of different types of clustering strategies is realized to find the most suitable method for clustering experts. Since Analytical Hierarchy Process is selected as the fundamental model, it becomes a reciprocal matrices' or eigenvectors' clustering task. Secondly, allocation strategies of information granularity to each expert along with weight to each subgroup are carefully designed and optimized. Information granularity is viewed as a design asset to provide experts with some flexibility to adjust their evaluation results, whereas weights reflect the importance degrees of different subgroups and further help to increase the consensus. A granulation-degranulation process is developed to evaluate the performance of a set of weights and granularities under a multi-criteria objective's guidance. Thirdly, a proper evolutionary optimization method like particle swarm optimization is redesigned to iteratively generate the best set of information granularities and weights under an equality constraint condition. A series of numerical studies on synthetic data and real-world data are executed to verify the effectiveness of our method. The results show same trend present across experts. [ABSTRACT FROM AUTHOR]

The object recognition problems realized in uncertain environments have played a paramount role in decision-making. In recent years, neutrosophic soft sets (NS-sets), a combination of soft and neutrosophic sets, have emerged as outstanding candidates in this field. If neutrosophic sets are used to handle problems involving imprecise, indeterminate, and inconsistent data, soft sets are used to deal with uncertainties that classical tools cannot control. This paper defines a new concept based on NS-sets, called the sequence of NS-sets (NSS-sequence). Their inclusions, special types, operations, distances are determined with reasonable, convincing, and well-proven properties. Furthermore, we also propose an algorithm for the decision-making problem on NS-sequence and apply it in medical diagnosis by a real-life experiment. Finally, intending to verify its validity and feasibility, we compare our algorithm to the algorithm for the decision-making problem on time NS-set (tNS-set) through the real-life mentioned earlier by Alkhazaleh. Our work also shows that the proposed algorithm on NS-sequence has the same results as that offered by Alkhazaleh, and the tNS-set is just a particular case of the NSS-sequence. [ABSTRACT FROM AUTHOR]

Granular computing, an emerging information processing paradigm transforming complex data into information granules at different scales so that different features and regularities can be revealed, offers an essential linkage between big data and decision making. By using innovative technologies of granular computing that transforms big data collections into information granules, we would be at position of recognizing and exploiting the meaningful pieces of knowledge present in data, and produce sound, and practically supported decisions. In this study, we first summarize a general scheme of big data–granular computing–decision making and then present a case study where we detect the important traffic event information by collecting and analyzing social media data, and transform them into probabilistic information granules that can be used for urban routing navigation. We propose a robust fastest path optimization model to incorporate the impact of traffic events and generate the optimal routing strategy. Real-life experiments are carried out in regional Chaoyang District, Beijing, as well as the backbone roadway network of Beijing, which illustrate the effectiveness of our proposed big data-driven decision-making method. Our study provides new evidence demonstrating that big data can be efficiently used to enhance decisions and granular computing with this regard. The concept of the proposed scheme can be easily extended for decision-making modeling in other domains. [ABSTRACT FROM AUTHOR]

Owing to well-documented advantages, multimodal biometrics systems form one of the visible and promising trends in the development of biometric systems. The results produced by the multimodal biometric system come as the aggregation of results produced by individual modalities encountered in the problem. In verification systems involving a single modality, a threshold has to be identified to separate the yes–no regions of the biometrics decision. However, in multimodal verification, there are four regions separated by two thresholds. In this paper, we introduce a concept of three-valued logic in a multimodal verification system in which four thresholds and nine regions have to be identified. The thresholds (regions) are optimized for a given number of parameters. Different structures of multimodal biometrics are tested for synthetic and publicly available datasets. [ABSTRACT FROM AUTHOR]

Supplier selection is a key issue in supply chain management, which directly impacts the manufacturer's performance. The problem can be viewed as a multiple attribute group decision making (MAGDM) that concerns many conflicting evaluation attributes, both being of qualitative and quantitative nature. Due to the increasing complexity and uncertainty of socio-economic environment, some evaluations of attributes are not adequately represented by numerical assessments and type-1 fuzzy sets. In this paper, we develop some linear programming models with the aid of multidimensional analysis of preference (LINMAP) method to solve interval type-2 fuzzy MAGDM problems, in which the information about attribute weights is incompletely known, and all pairwise comparison judgments over alternatives are represented by IT2FSs. First, we introduce a new distance measure based on the centroid interval between the IT2FSs. Then, we construct the linear programming model to determine the interval type-2 fuzzy positive ideal solution (IT2PIS) and corresponding attributes weight vector. Based on it, an extended LINMAP method to solve MAGDM problem under IT2FSs environment is developed. Finally, a supplier selection example is provided to demonstrate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic on-line fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables). [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple-attribute decision-making (MADM) problems based on traditional Maclaurin symmetric mean operator operating under hesitant fuzzy environment. The Maclaurin symmetric mean (MSM) operator is a classical mean type aggregation operator and has strong modeling capability in modern information fusion theory, which has particular advantages for aggregating multi-dimension arguments. The prominent characteristic of the MSM operator is that it can capture the interrelationship among the multi-input arguments. Motivated by the idea of MSM operator, we develop the hesitant fuzzy Maclaurin symmetric mean (HFMSM) operator for aggregating the hesitant fuzzy information. Some desirable properties such as monotonicity, boundedness, idempotency are studied. Furthermore, we have discussed some special cases with respect to different parameter values of the HFMSM operator in detail. For the situations where the input arguments have different importance, we further develop the weighted hesitant fuzzy Maclaurin symmetric mean operator to aggregate hesitant fuzzy information. Based on which, an approach to MADM problems with hesitant fuzzy information is developed. Finally, a practical example with paper quality evaluation of sciencepaper online in China is provided to illustrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the realm of decision making under uncertainty, the general approach is the use of the utility theories. The main disadvantage of this approach is that it is based on an evaluation of a vector-valued alternative by means of a scalar-valued quantity. This transformation is counterintuitive and leads to loss of information. The latter is related to restrictive assumptions on preferences underlying utility models like independence, completeness, transitivity etc. Relaxation of these assumptions results into more adequate but less tractable models. In contrast, humans conduct direct comparison of alternatives as vectors of attributes' values and don't use artificial scalar values. Although vector-valued utility function-based methods exist, a fundamental axiomatic theory is absent and the problem of a direct comparison of vectors remains a challenge with a wide scope of research and applications. In the realm of multicriteria decision making there exist approaches like TOPSIS and AHP to various extent utilizing components-wise comparison of vectors. Basic principle of such comparison is the Pareto optimality which is based on a counterintuitive assumption that all alternatives within a Pareto optimal set are considered equally optimal. The above mentioned mandates necessity to develop new decision approaches based on direct comparison of vector-valued alternatives. In this paper we suggest a fuzzy Pareto optimality (FPO) based approach to decision making with fuzzy probabilities representing linguistic decision-relevant information. We use FPO concept to differentiate 'more optimal' solutions from 'less optimal' solutions. This is intuitive, especially when dealing with imperfect information. An example is solved to show the validity of the suggested ideas. [ABSTRACT FROM AUTHOR]

Many large organizations have multiple large databases as they transact from multiple branches. Many important decisions are based on a set of specific items called the select items. Thus, the analysis of select items in multiple databases is an important issue. For the purpose of studying select items in multiple databases, one might need true global patterns of select items. Thus, we propose a model of mining global patterns of select items from multiple databases. A measure of overall association between two items in a database is proposed. We have extended the proposed measure for a database whose transactions contain items along with the quantities purchased. We have designed an algorithm based on proposed measure for the purpose of grouping the frequent items in multiple databases. In addition, we have studied properties of different measures proposed in this paper. Experimental results are presented for both real and synthetic databases. [ABSTRACT FROM AUTHOR]

In this study, we investigate the use of collective knowledge of independent classifiers (experts) in the area of face recognition. We formulate a hypothesis and provide compelling experimental evidence behind it that different image transformations can offer unique discriminatory information useful for face classification. We show that such discriminatory information can be combined in order to increase classification rates over those being produced by individual classifiers. In particular, we focus on contrast enhancement realized by histogram equalization and edge detection carried out with the use of the Sobel operator. We construct feature spaces emerging from linear and nonlinear methods of dimensionality reduction, namely Eigenfaces, Fisherfaces, kernel-PCA, and Isomap. Aggregation of classifiers is accomplished by majority voting and a Bayesian product rule. Extensive experimentation is conducted using the well-known FERET and YALE datasets. [ABSTRACT FROM AUTHOR]