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Abstract Sequential investment problem is a widespread issue in venture capital (VC) process and it is an important key for the success of VC as well. Not only the basic information of projects but also the sunk cost, the network of venture capitalists (VCs), etc., should be considered in the sequential decision-making. In this paper, we study the sequential investment from building a decision-making index system firstly. Then, because of complex and uncertain decision-making circumstance, it is hard for the VCs to give their evaluation information for each alternative as crisp numbers, but it may be easy for them to provide their preferences over each pair of alternatives. Motivated by which, the probabilistic hesitant fuzzy preferences (P-HFPs) are adopted by the VCs in their evaluation process. Moreover, considering the bounded rational psychological characteristic of the VCs in decision-making, the prospect theory has been introduced. Therefore, a prospect consensus with P-HFPs has been constructed due to the grouping of sequential decision-making. It has been applied to a practical case, and an expected consensus under rational framework has been used to analyze the case. The superiority of prospect consensus with P-HFPs has been correspondingly demonstrated in this paper through the comparative analyses of the expected consensus with P-HFPs, the prospect consensus with HFPs and the expected consensus with HFPs. [ABSTRACT FROM AUTHOR]

Categorization is necessary for many decision making tasks. However, the categorization process may interfere the decision making result and bring about the disjunction fallacy. To predict the interference effect of categorization, some models based on quantum cognition theory have been proposed. In quantum dynamical models, like the quantum belief-action entanglement (BAE) model, actions and beliefs are deemed to be entangled. However, the entanglement degree is an artificially defined parameter. In this paper, a new evidential dynamical (ED) model based on Dempster–Shafer (D-S) evidence theory and quantum dynamical modelling is proposed. Considering that sometimes people hesitate to make a decision, it is reasonable to extend the action states by introducing an uncertain state. In an evidential framework, categorization can influence the uncertain state in actions. The interference effect is measured by handling the uncertain state while no extra parameter is defined artificially. The proposed model is applied to the classical categorization decision-making experiments. Compared with the existing models, the number of free parameters in the ED model is less than the classical quantum models, and the ED model is more rational and simpler than an evidential Markov model. The model application results and discussions show the correctness and effectiveness of the ED model. Not only the interference effect of categorization on decision making results is explained and predicted, but also an inspiring dynamical decision making framework is proposed in this paper. We believe that the proposed ED model will bring more opportunities and will result in more applications in the future. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive learning framework that aims to enable agents with bounded rationality to learn from dynamic games and making decisions accordingly. Within this framework, agents lack complete knowledge of future payoffs but are capable of forming beliefs based on their recognitive abilities and past experiences. Each region involved dynamically adjusts its strategy by considering the tradeoff between the current payoff and belief updated. Based on this premise, we propose the Boundedly Rational Multiagent Learning (BRML) algorithm and provide proof of its convergence under given conditions. To demonstrate the practicality of the BRML algorithm, we employ it to two critical dynamic games: repeated cooperative and non-cooperative games. Our results indicate that groups with bounded rationality can achieve coordination through the behavior of agents with high recognitive ability. Furthermore, Pareto optimality is successfully attained in the coordination problems through the application of this multiagent adaptive learning framework. • This study contributes to the field of adaptive learning by presenting an approach that accommodates agents with bounded rationality. The proposed BRML algorithm enables dynamic adjustments of strategies based on the agents' limited knowledge of future payoffs, which successfully incorporates social learning and bounded rationality into multiagent adaptive learning in dynamics games. • The proof of convergence under given conditions establishes the theoretical soundness of the BRML algorithm. Overall, the proposed framework and algorithm provide a promising avenue for adaptive learning in multiagent systems. • The algorithm proposed is applied to the coordination problems, illustrating its practicality in achieving coordination and Pareto optimality. This paper offers a valuable contribution to the application of adaptive learning in multiagent systems, opening up avenues for further research and development. [ABSTRACT FROM AUTHOR]

Signature synthesis is a computation technique that generates artificial specimens which can support decision making in automatic signature verification. A lot of work has been dedicated to this subject, which centres on synthesizing dynamic and static two-dimensional handwriting on canvas. This paper proposes a framework to generate synthetic 3D on-air signatures exploiting the lognormality principle, which mimics the complex neuromotor control processes at play as the fingertip moves. Addressing the usual cases involving the development of artificial individuals and duplicated samples, this paper contributes to the synthesis of: (1) the trajectory and velocity of entirely 3D new signatures; (2) kinematic information when only the 3D trajectory of the signature is known, and (3) duplicate samples of 3D real signatures. Validation was conducted by generating synthetic 3D signature databases mimicking real ones and showing that automatic signature verifications of genuine and skilled forgeries report performances similar to those of real and synthetic databases. We also observed that training 3D automatic signature verifiers with duplicates can reduce errors. We further demonstrated that our proposal is also valid for synthesizing 3D air writing and gestures. Finally, a perception test confirmed the human likeness of the generated specimens. The databases generated are publicly available, only for research purposes, at. • Neuromotor-inspired model for kinematic synthesis based on the lognormality principle. • Flexible artificial generation of 3D on-air signatures. • Duplication of 3D on-air signatures. • Performance evaluation of 3D synthetic signature databases. • Perceptual evaluation of generated 3D on-air signatures. [ABSTRACT FROM AUTHOR]

This paper proposes a post-hoc explanation method for computing concept attribution in Fuzzy Cognitive Map (FCM) models used for scenario analysis, based on SHapley Additive exPlanations (SHAP) values. The proposal is inspired by the lack of approaches to exploit the often-claimed intrinsic interpretability of FCM models while considering their dynamic properties. Our method uses the initial activation values of concepts as input features, while the outputs are considered as the hidden states produced by the FCM model during the recurrent reasoning process. Hence, the relevance of neural concepts is computed taking into account the model's dynamic properties and hidden states, which result from the interaction among the initial conditions, the weight matrix, the activation function, and the selected reasoning rule. The proposed post-hoc method can handle situations where the FCM model might not converge or converge to a unique fixed-point attractor where the final activation values of neural concepts are invariant. The effectiveness of the proposed approach is demonstrated through experiments conducted on real-world case studies. [ABSTRACT FROM AUTHOR]

A High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers utilities of items (such as profits and margins) to discover interesting patterns from transactional databases. Several data structures, pruning strategies and algorithms have been proposed in the literature to efficiently mine high utility itemsets. Most of these works, however, do not consider itemsets with negative unit profits that provide greater flexibility to a decision maker to determine profitable itemsets. This paper aims to advance the state-of-the-art and presents a generalized high utility mining (GHUM) method that considers both positive and negative unit profits. The proposed method uses a simplified utility-list data structure for storing itemset information during the mining process. The paper also introduces a novel utility based anti-monotonic property to improve the performance of HUI mining. Furthermore, GHUM adapts key pruning strategies from the basic HUI mining literature and presents new pruning strategies to significantly improve the performance of mining. The proposed method is evaluated on a set of benchmark sparse and dense datasets and compared against a state-of-the-art method. Rigorous experimental evaluation is performed and implications of the key findings are also presented. In general, GHUM was found to deliver more than an order of magnitude improvement at a fraction of the memory over the state-of-the-art FHN method. [ABSTRACT FROM AUTHOR]

The three-way decision model is a topic of substantial research interest in the field of artificial intelligence, and many researchers have focused on to its feasibility and rationality. The tolerance and practicability of the three-way decision model are better than those of the two-way decision model. When the attribute value of each object in a domain is given, the formation of a three-way classification of the domain is a key issue. However, few studies have been conducted on establishing a three-way decision model with the given attribute values in the case where the number of objects in an accepted region is given. Therefore, in the model presented in this paper, both the uncertainty of attribute values and the cost of updating are fully considered. In this paper, first, a new concept of attribute ratio is defined to describe an object when the attribute value of the object is numerical, and then, a dynamic three-way decision model is established. Second, a feature extraction algorithm of attribute values is proposed, and a pair of decision thresholds of the dynamic three-way decision model is also obtained according to the given conditions. Then, in the case where the attribute values are updated, an example is provided to demonstrate how two-way classification results can be obtained in the dynamic decision-making process. Finally, the results of simulation experiments show that the proposed model is feasible and effective in practical applications. When the number of objects in an accepted region has been given, according to the updating strategy of attribute values, the three-way decision problems are successfully solved by the proposed model. [ABSTRACT FROM AUTHOR]

The research activities in group decision making have dramatically increased over the last decade. In particular, the application of multiple attribute decision-making methods to group decision-making problems occupies a vast area in the related literature. However, there is no systematic classification scheme for these researches. This paper presents a generic conceptual framework and a classification scheme for multiple attribute group decision-making methods. The proposed framework consists of three main stages: the structuring and construction stage, the assessment stage, and the selection/ranking stage, providing not only an outline for classification but also a road map for the researchers working on this topic. Furthermore, top cited papers are classified based on this classification scheme in order to clarify the state of the art and to identify future research directions. As a result, eight significant suggestions for future research are identified. [ABSTRACT FROM AUTHOR]

The original Pawlak's rough set approach based on indiscernibility relation (single granularity) has been extended to multigranulation rough set structure in the recent years. Multigranulation rough set approach has become a flouring research direction in rough set theory. This paper considers rough approximation of a fuzzy concept under the framework of multigranulation over two different universes of discourse, i.e., multigranulation fuzzy rough set models over two universes. We present three types of multigranulation fuzzy rough set over two universes by the constructive approach, respectively. Some interesting properties of the proposed models are discussed and also the interrelationships between the proposed models and the existing rough set models are given. We then propose a new approach to a kind of multiple criteria group decision making problem based on multigranulation fuzzy rough set model over two universes. The decision rules and algorithm of the proposed method are given and an example of handling multiple criteria group decision making problem of clothes ranking illustrates this approach. The main contribution of this paper is twofold. One is to establish the multigranulation fuzzy rough set theory over two universes. Another is to try presenting a new approach to multiple criteria group decision making based on multigranulation fuzzy rough set over two universes. The proposed models not only enrich the theory of multigranulation rough set but also make a tentative to provide a new perspective for multiple criteria group decision making with uncertainty. [ABSTRACT FROM AUTHOR]

RIPPER is certainly one of the best rule induction algorithms. In RIPPER, the order in which the rules are learned is important because the first rule to be fired determines the class of the instance. However, the correct class may be identified by another rule further down the list, which is ignored and, thus never examined. This paper offers a contribution to address the mentioned shortcoming. An Ant Colony Optimization (ACO) algorithm is developed for finding the optimal order of rules in the decision list. This algorithm is called ACO for Rule Induction (ACORI). To the best of our knowledge, this is the first paper that devises an optimization method to determine the (near) optimal order of rules in the decision list. The performance of the proposed algorithm is compared to that of RIPPER using 10 data sets. Experimental results and non-parametric statistical tests show that the proposed algorithm significantly outperforms the original RIPPER. [ABSTRACT FROM AUTHOR]

The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods. In this work we describe the methodology that won the ECBDL’14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems. [ABSTRACT FROM AUTHOR]

This paper deals with linguistic models that may prove useful for representing the information during decision making. Data extraction is a complex problem, especially when dealing with information coming from human beings (linguistic assertions, preferences, feelings, etc.) and several models have emerged to overcome difficulties in expressing the data. Among those models, we are interested in two of them: the 2-tuple semantic model and the 2-tuple symbolic model. In this paper we stress on a comparison between both models and we prove that links can be made between them. An interesting result is obtained: the 2-tuple semantic model can generate a partitioning that is identical to the one that would be generated thanks to the symbolic 2-tuple model. This permits to compare the models and mix the two when there is a need to use one model or another, depending on the applications, and then to reach a consensus. In closing, an example of the use is given to demonstrate the value of the method. [ABSTRACT FROM AUTHOR]

Attribute measures, used to evaluate the quality of candidate attributes, play an important role in the process of attribute reduction. They largely affect the computational efficiency of attribute reduction. Existing attribute measures are acted on the whole universe in complete decision systems. There are few studies on improving attribute reduction algorithms from the perspective of attribute measures in incomplete decision systems, which motivates the study in this paper. This paper proposes new attribute measures that act on a dwindling universe to quicken the attribute reduction process. In particular, the monotonicity guarantees the rationality of the proposed attribute measures to evaluate the significance of candidate attributes. On this basis, the corresponding attribute reduction algorithms are developed in incomplete decision systems based on indiscernibility relation and discernibility relation, respectively. Finally, a series of comparative experiments are conducted with different UCI data sets to evaluate the performance of our proposed algorithms. The experimental results indicate that the proposed algorithms are efficient and feasible. [ABSTRACT FROM AUTHOR]

Diagnosis is a critical activity in the PHM domain (Prognostics and Health Management) due to its impact on the downtime and on the global performances of a system. This activity becomes complex when dealing with large systems such as power plants, ships, aircrafts, which are composed of multiple systems, sub-systems and components of different technologies, different usages, and different ages. In order to ease diagnosis activities, this paper proposes to use a fleet-wide approach based on ontologies in order to capitalize knowledge and data to help decision makers to identify the causes of abnormal operations. In that sense, taking advantage of a fleet dimension implies to provide managers and engineers more knowledge as well as relevant and synthetized information about the system behavior. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. This paper presents a knowledge structuring scheme of fleets in the marine domain based on ontologies for diagnostic purposes. The semantic knowledge model formalized with an ontology allowed to retrieve data from a set of heterogeneous units through the identification of common and pertinent points of similarity. Hence, it allows to reuse past feedback experiences to build fleet-wide statistics and to search "deeper" causes producing an operation drift. [ABSTRACT FROM AUTHOR]

In emergency decision making, it can be difficult for decision-makers (DMs) to identify all possible scenarios due to a lack of information and the evolution of emergency situations. Therefore, this paper presents an incomplete probabilistic linguistic term set (InPLTS), which is a generalized hesitant fuzzy linguistic term set (HFLTS). The InPLTS can more appropriately describe a case in which a DM considers several possible linguistic terms with uncertain probabilities. Furthermore, this work extends the InPLTS to an incomplete probabilistic linguistic preference relation (InPLPR) and proposes a complete algorithm based on an emergency fault tree analysis (EFTA) to estimate missing entries of the InPLPR. The work also investigates the expected consistency, acceptable expected consistency, and consistency-improving methods for the reasonable application of the InPLPR. Then, a consistency-based emergency decision-making method using the InPLPR is proposed to address issues related to a lack of information, uncertainties and dynamic trends. In using this method, DMs can evaluate emergency alternatives of different possible scenarios with the InPLPR, and the impacts of different emergency responses on the evolution of emergencies can also be considered. Finally, the InPLPRs and the abovementioned method are applied to a public health emergency decision-making process to illustrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract We introduce collective decision-making models associated with influence spread under the linear threshold model in social networks. We define the oblivious and the non-oblivious influence models. We also introduce the generalized opinion leader–follower model (gOLF) as an extension of the opinion leader–follower model (OLF) proposed by van den Brink et al. (2011). In our model we allow rules for the final decision different from the simple majority used in OLF. We show that gOLF models are non-oblivious influence models on a two-layered bipartite influence digraph. Together with OLF models, the satisfaction and the power measures were introduced and studied. We analyze the computational complexity of those measures for the decision models introduced in the paper. We show that the problem of computing the satisfaction or the power measure is #P-hard in all the introduced models even when the subjacent social network is a bipartite graph. Complementing this result, we provide two subfamilies of decision models in which both measures can be computed in polynomial time. We show that the collective decision functions are monotone and therefore they define an associated simple game. We relate the satisfaction and the power measures with the Rae index and the Banzhaf value of an associated simple game. This will allow the use of known approximation methods for computing the Banzhaf value, or the Rae index to their practical computation. [ABSTRACT FROM AUTHOR]

Abstract In healthcare service systems, patients are not always served in the order they arrive, but are ranked with respect to their relative "importance" and "urgency" to the service system. We consider such a system where elective admission requests backlogged on a list wait to be assigned inpatient beds. To consolidate the performance of Classified Diagnose and Treatment in China, determining an optimal admission priority assignment policy for all waiting patients is vital. It is a complicated multi-criteria decision making (MCDM) problem involving both qualitative and quantitative criteria. Evaluating the admission priority of each patient is based on vague information or uncertain data in which significant dependence and feedback between the evaluation dimensions and criteria may exist. This paper applies a hybrid MCDM model that integrates the 2-tuple DEMATEL technique and the fuzzy VIKOR method to the elective admission control problem. It makes use of the modified 2-tuple DEMATEL to determine the relative weights of the evaluation criteria and the fuzzy VIKOR method to assess the alternatives (waiting patients) over the criteria. An empirical case in West China Hospital is presented to demonstrate the applicability of the proposed approach. Sensitivity analysis of the results by the proposed hybrid MCDM model and comparative analysis with other different approaches are presented. The results show that the proposed model is effective and provides insightful implications for hospital managers to refer. [ABSTRACT FROM AUTHOR]

Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is the tuning of their hyperparameters. In practice, selecting these hyperparameters is often done manually. Recently, the ML community has advocated for tuning hyperparameters through black-box optimization and developed state-of-the-art systems to do so. However, applying such systems to tune GBTs suffers from two drawbacks. First, these systems are not model-aware , rather they are designed to apply to a generic model; this leaves significant optimization performance on the table. Second, using these systems requires domain knowledge such as the choice of hyperparameter search space, which is an antithesis to the automatic experimentation that black-box optimization aims to provide. In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems. First, Mulch leverages powerful techniques in metalearning and multifidelity optimization to perform model-aware hyperparameter optimization. Second, it automates the process of learning performant hyperparameters by making intelligent decisions about the optimization search space, thus reducing the need for user domain knowledge. These innovations allow Mulch to identify good GBT hyperparameters far more efficiently—and in a more seamless and user-friendly way—than existing black-box hyperparameter tuning systems. • Mulch is a system developed to automate hyperparameter optimization of GBTs. • Mulch chooses the HPO search space, reducing the need for domain expertise. • Mulch uses metalearning and multifidelity optimization to accelerate performance. • Mulch delivers superior performance compared to black-box software like Optuna. [ABSTRACT FROM AUTHOR]

The Extended Belief Rule-Based (EBRB) system has been widely used to solve the real-world problems concerning with incompleteness, uncertainty, and ambiguity. However, EBRB is essentially a data-driven method, in which each rule is obtained from training data. Therefore, the generated extended belief rules may be severely biased when dealing with data with imbalanced classes. In this case, the number of the rules generated by the samples of majority classes (i.e., negative samples) may be much larger than those of minority classes (i.e., positive samples). Thus, the class imbalance may lead to significant biases in system decision-making. In order to resolve this problem, this paper proposes a novel EBRB system based on fuzzy C-means clustering (FCM-EBRB). First, we adopt FCM clustering to oversample the positive samples and undersample the negative ones, so as to achieve the balance between them. Next, this paper improves the construction method of EBRB and optimizes the system through an efficient parameter learning strategy. Finally, this paper conducts comprehensive comparison experiments on a binary classification synthetic dataset and 11 commonly used KEEL public class imbalance datasets. Experimental results show that the proposed method can effectively reduce the scale of the rule base and achieve high inference accuracy, especially for imbalanced data. [ABSTRACT FROM AUTHOR]

Abstract: As a hot topic, financial distress prediction (FDP), or called as corporate failure prediction, bankruptcy prediction, acts as an important role in decision-making of various areas, including: accounting, finance, business, and engineering. Since academic research on FDP has gone on for nearly eighty years, there are abundant literatures on this topic, which may appear chaotic to the researchers of the field and make them feel confused. This paper contributes to the current review researches by making a full summary, analysis and evaluation on the current literatures of FDP. The current literatures of FDP are reviewed from the following four unique aspects: definition of financial distress in the new century, FDP modeling, sampling approaches for FDP, and featuring approaches for FDP. By considering the new state-of-the-art techniques in this area, FDP modeling are classified and reviewed by the following groups: namely, modeling with pure single classifier, modeling with hybrid single classifier, modeling by ensemble techniques, dynamic FDP modeling, and modeling with group decision-making techniques. Sampling methods for FDP are classified and reviewed by the following paired groups, namely: training sampling and testing sampling, single industry sampling and cross-industry sampling, balanced sampling and imbalanced sampling. Featuring methods for FDP are categorized and reviewed by qualitative selection and combination of qualitative and quantitative selection. We comment on the current researches from the view of each category and propose further research topics. The review paper is valuable to guide research and application of the area. [Copyright &y& Elsevier]

Abstract: We establish in this paper the optimization model of group consensus of 2-tuple linguistic preferential relations (LPRGCO Model), put forward three kinds of solutions to this model, and discover in it the convergence of group consensus. To detect the LPRGCO Model, we first build two kinds of optimal matrices as standards to measure the group consensus of 2-tuple linguistic preference relations (LPRs). And to analyze consensus deviations, we then adopt three types of measures, namely, the individual degree of consistency regarding alternative decision pairs, the deviational degree of the group consensus regarding alternative decision pairs, and the degree of group consensus regarding the original 2-tuple LPRs. On the basis of the previous analysis we not only construct an optimization model to probe into the deviation of the group consensus of 2-tuple LPRs by minimizing the weighted arithmetic average of deviation degrees of individual consistency, but also point out three feasible solutions to this optimization model: the optimal solution, satisfactory solutions, and non-inferior solutions. Accordingly, we discover different conditions in terms of the three solutions. And hence, we can from the aforementioned discussion draw a conclusion that the deviation of group consensus either decreases or stays invariant as the number of decision makers (DM) increases. To expatiate on the practical value of the model proposed, we will display in this paper numerical examples. [Copyright &y& Elsevier]

Abstract With the development of big data and social computing, large-scale group decision making (LGDM) problems attract much attention and become merging with social networks or behavioral factors. In this paper, by considering the trust of social behavioral factor, a two-stage trust network partition algorithm is proposed to reduce the complex of LGDM problems. The large-scale decision makers (DMs) are classified into some leader–follower sub-networks through a network partition algorithm. And a solution method based on trust relationship is proposed to keep the independency of the sub-networks when one follower belongs to more than one leader. Next, weights of independent sub-networks and their individual members are computed, and are further used to aggregate the comprehensive decision information. Finally, alternatives of LGDM problems are sorted with the comprehensive decision information. An experiment with MovieLens data is given to illustrate the proposed LGDM algorithm. And a comparison analysis with general clustering method is provided to verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Fuzzy linguistic preference relations (FLPRs), which decision makers (DMs) use to express their pairwise comparison information, are suitable for modeling the qualitative aspects of problems. Ordinal consistency is the minimum condition that DM's preference values arenot self-contradictory. In this paper, we first define and study the ordinal consistency of the incomplete FLPRs. A novel algorithm is developed to judge whether an incomplete FLPR is ordinally consistent. The proposed algorithm can also identify all cycles of length 3 to n in the digraph of an incomplete FLPR. Based on the proposed algorithm and two rules, another algorithm is proposed to rectify the inconsistency of an incomplete FLPR and improve it so that it is ordinally consistent. Furthermore, the proposed algorithms can also be used to identify and rectify the ordinal inconsistency of the complete FLPRs. Finally, two examples are given, and comparisons with the existing methods are also provided to demonstrate the effectiveness and validity of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract Multiple attribute decision making (MADM) problems often consists of quantitative and qualitative attributes which can be assessed by numerical values and subjective judgments. Subjective judgments can be evaluated by linguistic variables, and both numerical values and subjective judgments can be accurate or uncertain. The evidential reasoning (ER) approach provides a process for dealing with MADM problems of both a quantitative and qualitative nature under uncertainty. The existing ER approach considers both benefit and cost attributes in the evidence combination process. In this paper, deviated interval and fixed interval attributes are introduced into ER based MADM approach and the frames of discernment for representing these two kinds of attributes are given. The transformation rules from the assessment values of deviated interval attributes to belief degrees in the ER structure are then studied. An ave-entropy based weight assignment method considering the risk preference of decision maker is also shown to deal with uncertain assessment situation, such as belief distribution with qualitative attribute and uncertain utility function. Some programming models to generate interval weights and utilities are constructed. The rationality and efficiency of the methods in supporting MADM problems are discussed. Two case studies are provided to demonstrate the applicability and validity of the proposed approaches and the potential in supporting MADM under uncertainty. [ABSTRACT FROM AUTHOR]

Abstract As an extension of Pawlak's rough sets, rough fuzzy sets is proposed to deal with the target concept which is typically fuzzy or uncertain. It is worthwhile to introduce cost-sensitive learning into granular computing, as the granularity with the optimal cost can be selected. In the terms of decision making, test cost and decision cost are the most popular cost types. In the sequential three-way decisions (S3WD) models, the granularity is sensitive to the test cost. Meanwhile, the accuracy is sensitive to the decision cost. Selecting an optimal cost-sensitive granularity for problem solving is helpful for achieving optimal results at the lowest total cost in S3WD. However, it is difficult to evaluate test cost precisely and objectively in real-life applications, and existing works only focus on searching for the total cost as the objective function. In this paper, we firstly present a sequential three-way decisions model with rough fuzzy sets (S3WDRFS). Then, for S3WDRFS and its three regions, the changing rules of their decision cost are revealed in a hierarchical granular structure. By considering user requirements, we propose an optimization mechanism to achieve the optimal cost-sensitive granularity selection based on S3WDRFS model. Finally, the experimental results demonstrate that exemplary optimal granularities can be obtained for high quality decision-making under certain constraints. [ABSTRACT FROM AUTHOR]

Abstract The purchasing process consists of multiple, non-isolated phases that describe the interactions between consumers and products. One of these phases is the evaluation of alternatives, in which the consumer compares the benefits that will be obtained from each product or service depending on a set of criteria. Recently, the rise of online ecosystems has fueled the development of new purchasing-decision prediction models (PDPM) based on the shared opinions, concerns, and expectations of customers. Commonly, these PDPMs are constructed as multiple criteria decision analysis (MCDA) methods, where experts ascertain the consumer's purchasing criteria, as well as the importance of them, by means of surveys and their own experience. Although relying on experts may be appropriate for identifying criteria, this approach is not suitable for assessing the importance of said criteria due to the large volume of information available on the Web. Furthermore, the preferences of consumers in online ecosystems evolve constantly. In this regard, we consider that the information about purchase-criteria importance should be derived exclusively from the opinions of consumers. However, the attitude and thought-process leading to purchases contain inherent relationships that consumers are not able to express explicitly; consequently, we explore the possibility to make use of the synergies between criteria along user preferences. This paper proposes a new PDPM that estimates the importance of the criteria and values the alternatives by only considering the opinions expressed by consumers in digital ecosystems, while using an expert-defined set of criteria. The proposed approach extracts the weights of both criteria and alternatives and determines the implicit synergies within them without the need of additional input. Lastly, it uses the Choquet integral for weight and valuation aggregation and determines a purchase ranking. This model has been tested in real examples, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

Abstract Intuitionistic fuzzy sets describe information from the three aspects of membership degree, non-membership degree and hesitation degree, which has more practical significance when uncertainty pervades qualitative decision problems. In this paper, we investigate the problem of ranking intuitionistic fuzzy preference relations (IFPRs) based on various non-linear utility functions. First, we transform IFPRs into their isomorphic interval-value fuzzy preference relations (IVFPRs), and utilise non-linear utility functions, such as parabolic, S-shaped, and hyperbolic absolute risk aversion, to fit the true value of a decision-maker's judgement. Ultimately, the optimization ordering models for the membership and non-membership of IVFPRs based on utility function are constructed, with objective function aiming at minimizing the distance deviation between the multiplicative consistency ideal judgment and the actual judgment, represented by utility function, subject to the decision-maker's utility constraints. The proposed models ensure that more factual and optimal ranking of alternative is acquired, avoiding information distortion caused by the operations of intervals. Second, by introducing a non-Archimedean infinitesimal, we establish the optimization ordering model for IFPRs with the priority of utility or deviation, which realises the goal of prioritising solutions under multi-objective programming. Subsequently, we verify that a close connection exists between the ranking for membership and non-membership degree IVFPRs. Comparison analyses with existing approaches are summarized to demonstrate that the proposed models have advantage in dealing with group decision making problems with IFPRs. [ABSTRACT FROM AUTHOR]

Abstract Hesitant fuzzy linguistic term sets (HFLTS) and dual hesitant fuzzy sets (DHFS) are two important branches of fuzzy mathematics, both of which have been widely applied in multiple-attribute decision-making (MADM) problems under uncertain environments. To humanize the decision-making process, the former deals with hesitant fuzzy linguistic terms in line with people’s common sense, and to reflect the nature of people’s hesitancy, the latter deals with both the membership and nonmembership hesitancy functions of fuzzy sets (FS). However, as the decision-making environment is increasingly complex, the characteristics of these two sets need to be combined to more precisely represent the fuzzy linguistic information. Therefore, this paper proposes a new extension of the HFLTS concept, i.e., dual hesitant fuzzy linguistic term set (DHFLTS), to highlight the importance of the nonmembership degree for HFLTSs. Some properties for the DHFLTS are given. Motivated by information energy, the information energy for DHFLTS and the correlation coefficient between DHFLTSs as well as the weighted correlation coefficient are defined. Finally, a supplier selection problem is given to demonstrate the feasibility and superiority of this method. [ABSTRACT FROM AUTHOR]

Abstract Hesitant fuzzy set (HFS) has been a very popular and useful tool to express the uncertainty of decision makers (DMs) when they could not give a determination among several different values in the process of representing preferences. In this paper, we mainly focus on digging the potential/deeper uncertain information of the DMs when they use HFSs to express their evaluation information in the process of decision making. As a result, a new fuzzy information form called normal wiggly hesitant fuzzy set (NWHFS) is proposed to assist the DMs in expressing their evaluation information more completely and improve the rationality of the decision-making result. The NWHFS can keep the hesitant fuzzy information and the deeper uncertain information dug in the hesitant fuzzy information. After that, we propose a score function of normal wiggly hesitant fuzzy information to distinguish the NWHFSs. Moreover, to understand and apply the NWHFSs easily, we investigate some properties of the NWHFSs and propose two preliminary aggregation operators of the NWHFSs. Finally, we use a numerical example involving environmental quality evaluation to verify the efficiency and applicability of the NWHFSs in decision-making and make a comparison between the proposed method and the existing decision-making method. [ABSTRACT FROM AUTHOR]

Large-scale group decision making (LSGDM) or complex group decision making (GDM) problems are very commonly encountered in actual life, especially in the era of data. At present, double hierarchy hesitant fuzzy linguistic term set is a reasonable linguistic expression when describing some complex linguistic preference information. In this paper, we develop a consensus reaching process for LSGDM with double hierarchy hesitant fuzzy linguistic preference relations. To ensure the implementation of consensus reaching process, we also propose the similarity degree-based clustering method, the double hierarchy information entropy-based weights-determining method and the consensus measures. Finally, we apply our model to deal with a practical problem that is to evaluate Sichuan water resource management and make some comparisons with the existing approaches. [ABSTRACT FROM AUTHOR]

Attribute weight assignment plays a key role in multiple attribute decision making (MADM). For the issue of labeled multiple attribute decision making (LMADM), the existing methods of attribute weight determination that have been well developed for MADM usually ignore or do not take full advantage of the supervisory function of labels. As a result, the weights produced by these methods may not be ideal in practice. To make up for this deficiency, this paper develops an objective method based on Bayes risk. Specifically, the LMADM problem is first put forward, then a Gaussian kernel based loss function is proposed to cope with the drawback that the loss function in Bayes risk is usually determined by experts. Meanwhile, Mahalanobis distance and fuzzy neighborhood relationship are employed to measure the fuzziness of data set. Finally, a number of experiments, including the comparison experiments on UCI data and the effectiveness evaluation of fighter, are carried out to illustrate the superiority and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a malware detection system for smartphones based on studying the dynamic behavior of suspicious applications. The main goal is to prevent the installation of the malicious software on the victim systems. The approach focuses on identifying malware addressed against the Android platform. For that purpose, only the system calls performed during the boot process of the recently installed applications are studied. Thereby the amount of information to be considered is reduced, since only activities related with their initialization are taken into account. The proposal defines a pattern recognition system with three processing layers: monitoring, analysis and decision-making. First, in order to extract the sequences of system calls, the potentially compromised applications are executed on a safe and isolated environment. Then the analysis step generates the metrics required for decision-making. This level combines sequence alignment algorithms with bagging, which allow scoring the similarity between the extracted sequences considering their regions of greatest resemblance. At the decision-making stage, the Wilcoxon signed-rank test is implemented, which determines if the new software is labeled as legitimate or malicious. The proposal has been tested in different experiments that include an in-depth study of a particular use case, and the evaluation of its effectiveness when analyzing samples of well-known public datasets. Promising experimental results have been shown, hence demonstrating that the approach is a good complement to the strategies of the bibliography. [ABSTRACT FROM AUTHOR]

Abstract: The paper treats and handles nuclear safeguard evaluations by using a linguistic aggregation approach. The overall evaluation is based on a hierarchical analysis of a state’s nuclear activities on the basis of the IAEA physical model. In this framework, we analyse three kinds of weight information, i.e., belief degrees of linguistic evaluation values, weights of IAEA experts about indicators and strengths of indicators, where, different belief degrees of linguistic evaluation values correspond with different uncertain evaluations due to lack of complete information or conflict information. To aggregate these linguistic evaluation values, we propose a weighted linguistic aggregation operator, and discuss it’s properties. Based on the weighted linguistic aggregation operator, we firstly use belief degrees of linguistic evaluation values and weights of IAEA experts to aggregate linguistic evaluation values of every indicator, then, we use strengths of indicators to aggregate linguistic evaluation values of indicators, and obtain linguistic evaluation value of every sub-factor, finally, we combine linguistic evaluation values of sub-factors to make the overall evaluation. Compared with existed approaches, we arrange linguistic evaluation values according to their belief degrees, and aggregated results of indicators are vectors of linguistic evaluation values in order of belief degree, this is different with cumulative belief degree of linguistic evaluation values, moreover, in the aggregation process, we analyse the aggregated results of “no information” and “conflict information”. We compare our method with some existed methods for nuclear safeguard evaluations in an example, which supports and clarifies the method of this paper. [Copyright &y& Elsevier]

Abstract: This paper presents an agent-based framework for modeling and deploying usiness-to-usiness () applications, where autonomous agents act on behalf of the individual components that form these applications. This framework consists of three levels identified by strategic, application, and resource, with focus in this paper on the first two levels. The strategic level is about the common vision that independent businesses define as part of their decision of partnership. The application level is about the business processes that get virtually combined as result of this common vision. As conflicts are bound to arise among the independent applications/agents, the framework uses a formal model based on computational argumentation theory through a persuasion protocol to detect and resolve these conflicts. In this protocol, agents reason about partial information using partial arguments, partial attack, and partial acceptability. Agents can then jointly find arguments that support a new solution for their conflicts, which is not known by any of them individually. Termination, soundness, and completeness properties of this protocol are provided. Distributed and centralized coordination strategies are also supported in this framework, which is illustrated with an online-purchasing example. [ABSTRACT FROM AUTHOR]

Abstract: Information axiom, one of two axioms of axiomatic design methodology which is proposed to improve a design, is used to select the best design among proposed designs. In the literature, there are a lot of studies related to using of information axiom for the solution of decision making problems. Moreover, applications of information axiom have been increasing day by day. However, calculation procedure of information axiom is not only incommodious but also difficult for decision makers. In this paper, a decision support system (DSS) based on fuzzy information axiom (FIA) is developed in order to make this decision procedure easy. The developed system consists of a knowledge base module including facts and rules, inference engine module including FIA and aggregation method, and a user interface module including entrance windows. The main aim of this study is to present a DSS tool to help the decision makers to solve their decision problems by modifying data-base of the program. In this paper, an application procedure will be presented based on the optimal selection of location for emergency service to illustrate the implementation procedure of the proposed model. [Copyright &y& Elsevier]

Abstract: This paper is concerned with a method for multiple attribute decision making under fuzzy environment, in which the preference values take the form of triangular fuzzy numbers. Based on the idea that the attribute with a larger deviation value among alternatives should be assessed a larger weight, a linear programming model about the maximal deviation of weighted attribute values is established. Therefore, an approach to deal with attribute weights which are completely unknown is developed by using expected value operator of fuzzy variables. Furthermore, in order to make a decision or choose the optimum alternative, an expected value method is presented under the assumption that attribute weights are known fully. The method not only avoids complex comparing for fuzzy numbers, but also has the advantages of simple operation and easy calculation. Finally, a numerical example is used to illustrate the proposed approach at the end of this paper. [Copyright &y& Elsevier]

Meticulous tuning of geometry parameters turns pivotal in improving performance of antenna systems. It is more and more often realized using formal optimization methods, which is demonstrably the most efficient way of handling multiple design variables, objectives, and constraints. Although in some cases a need for launching global search arises, a typical design scenario only requires local optimization, especially when a decent initial design can be rendered using engineering experience or parametric studies. At the same time, antenna optimization is typically conducted using full-wave electromagnetic (EM) simulations, which entails considerable computational expenses. In this paper, we introduce a novel procedure for expedited antenna tuning. Its fundamental mechanism is to restrict the antenna response sensitivity updates to the selected dominant directions within the parameter space, determined based on the problem-specific knowledge, in particular, the estimated changes of antenna characteristics when moving across one-dimensional affine subspaces spanned by these directions. Thus, the said dominant directions affect the most the responses of the antenna structure under design as assessed using the introduced metrics. The decision making process concerning the number of directions to be used relies on quantification of the aggregated system response variability metrics. The proposed approach is demonstrated by means of several antenna structures and benchmarked against conventional trust-region algorithm, but also its accelerated versions. The results indicate considerable (up to over 60%) speedup over the reference procedure without noticeable quality degradation. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a new knowledge acquisition method using a generic design environment where context-sensitive knowledge is used to build specific DSS for rural business. Although standard knowledge acquisition methods have been applied in rural business applications, uptake remains low and familiar weaknesses such as obsolescence and brittleness apply. We describe a decision support system (DSS) building environment where contextual factors relevant to the end users are directly taken into consideration. This “end user enabled design environment” (EUEDE) engages both domain experts in creating an expert knowledge base and business operators/end users (such as farmers) in using this knowledge for building their specific DSS. We document the knowledge organisation for the problem domain, namely a dairy industry application. This development involved a case-study research approach used to explore dairy operational knowledge. In this system end users can tailor their decision-making requirements using their own judgement to build specific DSSs. In a specific end user’s farming context, each specific DSS provides expert suggestions to assist farmers in improving their farming practice. The paper also shows the environment’s generic capability. [Copyright &y& Elsevier]

Decision-making is the most important business activity and becomes more complex in the current big-data situation. Most organizational decision-making is made in a group and has the data analytics function to seek specific answers for specific purposes. Multi-attribute group decision-making (MAGDM) methods provide effective support to decision groups by evaluating and integrating individual group members opining. However, current MAGDM methods often suffer from the problem of opining data and decision environment uncertainty, which is particularly severe in a large decision group or a newly decision problem. As a solution, rough sets and fuzzy sets have been applied in MAGDM to deal with data and decision process uncertainties. Although a lot of efforts have been made in applying rough sets and fuzzy sets to deal with data and decision process uncertainties the area, the disadvantage of rough set models in classification accuracy when similar class as basic knowledge granularity has not been well solved yet. This paper aims to solve this problem by introducing a new concept-maximal consistent block (MCB) and multi-granulation decision-theoretic rough set (MCB-MDTRS) models. Firstly, it establishes a binary tolerance relation on the universe of discourse, defines generalized MCBs, and introduces pessimistic DTRS based on MCBs. Secondly, it extends objective set to a fuzzy environment and proposes four MCB-MDTRFS models with consideration of the weight of each attribute. The steps of the proposed fuzzy MAGDM methods are carefully described in detail. Different from existing fuzzy MAGDM methods, the weight of each attribute is considered in the determination of the positive ideal decision objective and negative ideal decision objective in this paper, named by qualified and unqualified (fuzzy) sets. We solve the vague problem of individual preference evaluation and the uncertainty of setting ideal decision objectives by using the advantages of fuzzy set and rough set theory. Finally, it takes emergency plan selection as a case study to analyze the effectiveness of our methods and compare with other fuzzy MAGDM methods. Our methods outperform the selection of basic knowledge granularity, the determination of ideal decision objective sets and the ranking method, so as to increase the reliability and accuracy of ranking evaluation index. [ABSTRACT FROM AUTHOR]

This paper develops an integrated trapezoidal interval type-2 fuzzy (TrIT2F) technique for democratic–autocratic multi-criteria group decision making based on best–worstmethod (BWM) and VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje), which is called TrIT2F-BW-VIKOR. In this technique, the pairwise comparisons and evaluations are represented by trapezoidal interval type-2 fuzzy sets (TrIT2FSs). The existing definition of TrIT2FS is perfected by adding two rational constraints proposed in this paper. A weight-normalizing theorem is initiated to normalize the TrIT2F weights. To determine the TrIT2F weights of junior decision makers (JDMs) and criteria, the classical BWM is extended into TrIT2F environment, which is called TrIT2F-BWM. In this TrIT2F-BWM , the weight-normalizing theorem is applied to normalize the TrIT2F weights, a consistency ratio is designed to check the reliability of the obtained TrIT2F weights. Based on the determined weights of JDMs and criteria, an extended VIKOR is developed to rank alternatives. The proposed technique can not only effectively retain the inherent fuzzy information of TrIT2FSs, but also flexibly handle different decision situations. The validity of the proposed technique is demonstrated with a makeshift (fangcang) hospital selection example on COVID-19. Some sensitivity and comparison analyses are provided to show the stability, flexibility, and superiorities of the proposed technique. [ABSTRACT FROM AUTHOR]

Trust and reputation systems are one of the most important tools for decision making which are able to predict the reliability of an agent, taking into account the past experience and received recommendations. However, the effectiveness of these systems may be greatly reduced in hostile environments with the presence of malicious attackers. Despite the increasing use of these systems and the importance of their robustness in hostile environments, So far the most efforts on reinforcing of these systems have been focused on trust computation models, not on the trust based decision making. This paper discusses the importance and impact of decision-making on the robustness of trust systems in hostile environments and shows its great impact in this regard. In the paper, the most important trust based decision-making methods are investigated using formal verification methods in different attack scenarios (including maliciousness and selfishness) and the results are analyzed to find the strengths and weaknesses of each one. Considering the obtained results, the paper also proposed a new approach for trust-based decision making. The case studies show that the proposed approach is able to improve the robustness of trust and reputation models in all different attack scenarios. • Trust based decision-making methods formally verified in hostile environments. • The weaknesses of current methods against selfishness and maliciousness are explored. • A novel decision-making method is proposed that improves the robustness. [ABSTRACT FROM AUTHOR]

Attribute weight assignment plays an important role in multiple attribute decision analysis (MADA). When the performances of alternatives on each attribute are expressed by distributions instead of single values, how to use the differences among the performances to obtain attribute weights is an interesting but difficult issue. To address this issue, in this paper, we propose a method for obtaining attribute weights from discriminating power in belief distributions. With the consideration of the differences among the utilities of all assessment grades used to profile belief distributions, the dissimilarity based discriminating power, the standard deviation based discriminating power and the Gini's mean difference based discriminating power of the performances of all alternatives on each attribute are constructed to determine three sets of respective weights of attributes. They are convexly combined using three coefficients to generate integrated weights of attributes. To relieve the burden on a decision maker to provide precise values for the three coefficients, they are allowed to change between 0 and 1, as long as their sum is equal to 1. Under such constraints on the three coefficients, an optimization model is constructed to determine the minimum and maximum expected utilities of each alternative. From the expected utilities, all alternatives are then compared using Hurwicz rule with the provided optimism degree interval to generate solutions to MADA problems. The transitivity of the comparison outcomes among three alternatives under a given optimism degree interval is theoretically analyzed to guarantee the rationality of the outcomes. A focal form selection problem is investigated to demonstrate the applicability and validity of the proposed method. [ABSTRACT FROM AUTHOR]

The complicated micro structures, macro emergences and dynamic evolutions in a supply chain network pose challenges to decision making for solving operational problems for the network's performance improvement. Most of these problems are complicated since various factors and their complicated relationships are involved. Success of this decision making relies on efficient business analytics based on the comprehensive and multi-dimensional data related to the static attributes and dynamic operations of the network. To confront the challenges, this paper proposes to explore a methodology of data-driven decision making for supply chain networks. In this methodology, a data-granularity model of a supply chain network is developed to standardize the data form for decision making. A four-dimensional-flow model of a supply chain network is proposed to satisfy the data requirements for decision making that are defined in the data-granularity model. Agent-based computational experiment is employed to support the generation of a comprehensive operational dataset of a supply chain network and to verify the solutions generated in decision making. Integrating these models, a data-driven decision-making framework for supply chain networks is proposed. In the framework, a new decision-making mode of “problem definition - business analytics - solution verification - parameter adjustment” is proposed. Oriented towards domain knowledge in supply chain networks, two approaches of business analytics—mapping analysis and correlation analysis—are presented. Finally, a case of a five-echelon manufacturing supply chain network is studied with the methodology. The findings indicate that the proposed methodology, models and framework are effective in supporting the data-centric decision making for solving complicated operational problems in supply chain networks and provide the networks’ managers or member enterprises with an effective tool to generate unbiased and efficient decisions for the networks’ performance improvement. [ABSTRACT FROM AUTHOR]

This paper aims at introducing a new method for Kansei data modelling, which is basically based on the linguistic interpretation of Kansei data and the probabilistic semantics of fuzzy sets. We first interpret a semantic differential scale used in Kansei experiment as a linguistic variable and then propose a new method of how the Kansei value of products can be represented as a probability distribution on the set of Kansei linguistic labels of the linguistic variable. We further extend this Kansei data modelling approach to the cases where the Kansei value is imprecisely given as an interval or a fuzzy number. Furthermore, the proposed approach for modelling Kansei data will be used to develop a target-based model for consumer-oriented evaluation. Finally, a case study for consumer-oriented evaluation of hand-painted Kutani cups, a traditional product in Ishikawa, Japan, is conducted to show the applicability of the proposed Kansei data modelling approach. [ABSTRACT FROM AUTHOR]

Rough set theory is a powerful mathematical tool to deal with uncertainty in data analysis. Interval-valued information systems are generalized models of single-valued information systems. Recently, uncertainty measures for complete interval-valued information systems or complete interval-valued decision systems have been developed. However, there are few studies on uncertainty measurements for incomplete interval-valued information systems. This paper aims to investigate the uncertainty measures in incomplete interval-valued information systems based on an α -weak similarity. Firstly, the maximum and the minimum similarity degrees are defined when interval-values information systems are incomplete based on the similarity relation. The concept of α -weak similarity relation is also defined. Secondly, the rough set model is constructed. Based on this model, accuracy, roughness and approximation accuracy are given to evaluate the uncertainty in incomplete interval-valued information systems. Furthermore, experimental analysis shows the effectiveness of the constructed uncertainty measures for incomplete interval-valued information systems. [ABSTRACT FROM AUTHOR]

This paper introduces actionable strategies to the trisecting-and-acting three-way decision model. We present four change-based actionable models according to action benefit and action cost. Two of the four models provide the bounds of the cost and benefit and the other two models quantify the maximum benefit under limited cost and the minimum cost for a desired benefit, respectively. We design and analyze algorithms for these models. Finally, we report experimental results to support the proposed models. [ABSTRACT FROM AUTHOR]

The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions. [ABSTRACT FROM AUTHOR]

Multigranulation rough sets have attracted more and more attentions in recent years. In real-life applications, with the development of information technology, the attribute values often dynamically evolve over time. How to update useful knowledge is of great importance for dynamic information systems. Approximations of a concept are fundamental concepts of multigranulation rough sets, which need to be updated incrementally while refining or coarsening attribute values. Motivated by the requirements of dynamic knowledge acquisition due to refining or coarsening attribute values, in this paper, we present the dynamic mechanisms for updating approximations in multigranulation rough sets while refining or coarsening attribute values. Then, the corresponding dynamic algorithms for updating multigranulation approximations are designed on the basis of the proposed mechanisms. Extensive experiments on six data sets from UCI demonstrate that the proposed dynamic algorithms for updating approximations in multigranulation rough sets are more effective in comparison with the static algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we combine context-based Meta-Reinforcement Learning with task-aware representation to efficiently overcome data-inefficiency and limited generalization in the hyperparameter optimization problem. First, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process for unseen tasks. Second, the task properties are inferred on-line, which includes not only the dataset representation but also the task-solving experience, thus encouraging the agent to explore in a much smarter fashion. Third, we employ amortized meta-learning to meta-train the agent, which is simple and runs faster than the gradient-based meta-training method. Experimental results suggest that our method can search for the optimal hyperparameter configuration with limited computational cost in a reasonable time. [Display omitted] • A task encoder is proposed to achieve the task inference. • The rich task-specific information allows the agent to make better decision. • An amortized meta-learning approach is proposed to accelerate the meta-training. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) aims to support decision-makers more effectively. This paper presents modifications for the criteria ranking methods, CRITIC and Distance-correlation CRITIC (D-CRITIC). These modifications involve the normalization procedure to silence criteria attribution by utilizing fuzzy logic and the hamming distance. Also, some adjustment to the weighting technique of the CRITIC method is done to capture linear and nonlinear correlations more precisely. These modifications that are applied to the CRITIC method by considering some reference point (RP), produced our proposed (M-CRITIC-RP) method. An illustrative example to rank performance criteria of display advertisement for a digital marketing campaign is used to demonstrate the method's workability. Comparative analysis with appropriate MCDM methods reveals that the distance correlation values for the weights of the M-CRITIC-RP and comparative methods exhibit higher consistency than those between other criteria ranking methods. Moreover, M-CRITIC-RP has a high average value of Spearman rank statistic, indicating high rank consistency. Regarding accuracy, the Symmetric Mean Absolute Percentage Error (sMAPE) and the Log Accuracy Ratio (LAR) error metrics show lower values for M-CRITIC-RP in comparison to the corresponding values of other criteria ranking methods. M-CRITIC-RP demonstrates sensitivity with changes in the number of alternatives and also to variations in the RPs. The results of the sensitivity analysis guide the analyst to pay ample attention when selecting the RP. Findings also suggest considering a sufficient sample size for better accuracy and consistency of weights and ranks. Several implications were discussed along with advantages and limitations to be addressed for future work. [ABSTRACT FROM AUTHOR]