Three-way Decisions Research Articles

Regret-based domination three-way decision-making model with circular spherical fuzzy Mahalanobis distance

This article proposes a revolutionary three-way multi-attribute model in response to the increased complexity and risk in decision-making processes. This model combines three-way decision rules with the influential behavioral theories of regret and prospect. To address uncertainties in evaluation data, a circular spherical fuzzy set is seamlessly integrated into the information system, noting that this method can also be smoothly applied to disc spherical fuzzy sets (D-SFSs). Moreover, a pioneering method utilizing correlation coefficients and variances is introduced for attribute weighting. The conventional Mahalanobis distance is extended to a circular, spherical, fuzzy Mahalanobis distance. A novel approach is developed to estimate conditional probabilities based on the distinct regret-based dominating and rejoice-based dominating relations in circular spherical fuzzy sets (C-SFSs). Additionally, three scoring systems based on prospect theory yield despondent, expectant, and balanced decision-making strategies and provide priceless insights into various decision-maker mindsets. Furthermore, to illustrate the practical application of these methods with D-SFSs information, an example has been solved utilizing D-SFSs.

A new probabilistic linguistic decision-making process based on PL-BWM and improved three-way TODIM methods

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, which allow decision-makers to label linguistic information with different probabilities. In this paper, a method based on a PLTS is proposed to address multi-criteria decision-making problems (MCDM). We develop the theory of PLTSs and put forward a novel best–worst method (BWM), termed PL-BWM, based on PLTS. Our method fully reflects the preference information of decision-makers and accurately provides the importance level of the criteria. The combined weight of the criteria is obtained by merging PL-BWM-based subjective weights and similarity minimization-based objective weights. Upon introducing a three-way decision system to improve the TODIM method, a novel three-way TODIM method is proposed and showcased on an optimal new energy vehicle selection problem. The effectiveness and accuracy of the proposed method are verified by sensitivity analysis and comparative analysis. Our approach paves the way for new developments in solving MCDM problems and for novel applications in otherwise difficult ranking problems.

A three-way decision method with probability dominance relation in interval-valued hesitant fuzzy environment for marine steam turbine fault diagnosis

A three-way decision method with probability dominance relation in interval-valued hesitant fuzzy environment for marine steam turbine fault diagnosis

A Hybrid Credit Risk Evaluation Model Based on Three-Way Decisions and Stacking Ensemble Approach

A Hybrid Credit Risk Evaluation Model Based on Three-Way Decisions and Stacking Ensemble Approach

Multi-Attribute Three-Way Decision Approach Based on Ideal Solutions under Interval-Valued Fuzzy Soft Environment

The interval-valued fuzzy soft set (IVFSS) model, which combines the benefits of the soft set model with the interval-valued fuzzy set (IVFS) model, is a growing and effective mathematical tool for processing hazy data. In detail, this model is characterized by symmetry, which has the lower and upper membership degree. The study of decision-making based on IVFSS has picked up more steam recently. However, existing multi-attribute decision-making (MADM) methods can only sort alternative schemes, but are not able to classify them, which is detrimental to decision-makers’ efficient decision-making. In this paper, we propose a multi-attribute three-way decision-making (MATWDM) algorithm based on ideal solutions for IVFSS. MATWDM is extended to the IVFSS environment by incorporating the concept of the ideal solution, offering a more adaptable and comprehensive approach for addressing uncertain MADM issues. The method not only obtains the ranking results of the alternatives, but also divides them into acceptance domain, rejection domain, and delayed-decision domain, which makes the decision results more reasonable and effective, facilitating decision-makers to make better decisions. We apply the proposed three-way decision algorithm to two practical cases as diverse as mine emergency decision and Homestay selection decision. Additionally, the effectiveness and viability of the suggested method are confirmed by experimental findings.

A fusion model for multiple partial-order quotient spaces based on three-way decision

With a quotient space, a complex problem can be transformed into an appropriate granularity for processing. However, within a quotient space, there are no partial order relations between information granules. In practical decision-making, it may be necessary to deal with quotient spaces that have partial order relations between information granules. In this paper, first, the concept of a partial-order quotient space is proposed and some of its properties are studied. Second, a fusion model for multiple partial-order quotient spaces based on three-way decision is proposed, and some related theorems are proved. Finally, a case study of teacher evaluation, an experiment on real data, and several simulation experiments are presented to demonstrate the practicality of the proposed model in this paper.

A novel deep domain adaptation method based on three-way decisions for identifying autistic patients

Combining machine learning with functional magnetic resonance imaging (fMRI) technology to build an effective autism recognition model has become a research hotspot. However, the performance of existing machine learning models is highly dependent on large-scale labelled data, and the cost of fMRI data acquisition and labelling limits the application of models in autism recognition. Although the availability of multisite data increases the sample size, the resolution, image contrast, and noise level of fMRI data acquired at different sites vary considerably due to the different scanning devices or scanning protocols used. The heterogeneity of fMRI data between sites leads to limited generalisation of the trained models. To tackle this problem, we propose a deep domain adaptation approach based on three-way decisions. First, we introduce a pseudolabel optimisation method based on three-way decisions, which fully considers the structural information of the target domain and makes secondary decisions on the uncertain objects in the target domain, thus improving the accuracy of the pseudolabel in the target domain. Then, we construct a new autism recognition network using a stacked autoencoder and combine the proposed pseudolabel optimisation model to adapt the domain shift. Finally, the self-training strategy is used to retrain the network after domain adaptation to improve the generalisability of the model to the target domain. Experimental results on several cross-domain autism recognition tasks show the effectiveness of the proposed method.

Three-way decision-based label integration for crowdsourcing

In crowdsourcing learning, label integration is often used to infer instances’ integrated labels from their multiple noisy labels. However, almost all existing label integration algorithms apply the same strategy to infer different instances’ integrated labels, which limits their performance. This paper argues that different instances should enjoy different label integration strategies alone. Thanks to the three-way decision theory, a three-way decision-based label integration (TDLI) algorithm is proposed. In TDLI, we at first evaluate the label qualities of each instance and its K-nearest neighbors, and then utilize them to divide the whole crowdsourced datasets into three disjoint subsets, called positive set, boundary set and negative set, respectively. For each instance in the positive set, we directly apply the simplest majority voting (MV) to infer its integrated label. For each instance in the boundary set, we absorb its K-nearest neighbors’ multiple noisy labels to infer its integrated label by the weighted MV. For each instance in the negative set, we merge the positive and boundary sets to train a classifier to infer its integrated label by fusing its own multiple noisy label distribution and the predicted label distribution. Extensive experiments demonstrate that TDLI distinctly outperforms all the other existing label integration algorithms used to compare.

Intuitionistic Fuzzy Sequential Three-Way Decision Model in Incomplete Information Systems

As an effective method for uncertain knowledge discovery and decision-making, the three-way decisions model has attracted extensive attention from scholars. However, in practice, the existing sequential three-way decision model often faces challenges due to factors such as missing data and unbalanced attribute granularity. To address these issues, we propose an intuitionistic fuzzy sequential three-way decision (IFSTWD) model, which introduces several significant contributions: (1) New intuitionistic fuzzy similarity relations. By integrating possibility theory, our model defines similarity and dissimilarity in incomplete information systems, establishing new intuitionistic fuzzy similarity relations and their cut relations. (2) Granulation method innovation. We propose a density neighborhood-based granulation method to partition decision attributes and introduce a novel criterion for evaluating attribute importance. (3) Enhanced decision process. By incorporating sequential three-way decision theory and developing a multi-level granularity structure, our model replaces the traditional equivalent relation in the decision-theoretic rough sets model, thus advancing the model’s applicability and effectiveness. The practical utility of our model is demonstrated through an example analysis of “Chinese + vocational skills” talent competency and validated through simulation experiments on the UCI dataset, showing superior performance compared to existing methods.

Fuzzy implications-based transformation approaches from semi-three-way decision spaces to three-way decision spaces and their applications

Three-way decision spaces, as an important component of three-way decisions, greatly enrich their theoretical development and application prospects. Meanwhile, fuzzy implications, as a vital class of fuzzy logic connectives, have made great contributions to the solution of practical problems, especially complex decision-making problems. This paper considers the collaborative effect of the two, which inject new vitality into the theoretical development and application prospects of fuzzy implications and three-way decision spaces. As a vital component of three-way decision spaces, (semi-)decision evaluation functions have been widely studied based on fuzzy logic connectives and become a research hotspot. Specifically, this paper focuses on fuzzy implications-based transformation approaches from semi-three-way decision spaces to three-way decision spaces and their applications. Firstly, we present some novel fuzzy implications-based transformation approaches from semi-decision evaluation functions to decision evaluation functions, and construction approaches of semi-decision evaluation functions involving the existing semi-decision evaluation functions, fuzzy sets, interval-valued fuzzy sets and fuzzy relations. Secondly, we discuss the relationship between our approaches and the known construction approaches of three-way decision spaces. Notably, our approaches cover all existing approaches except the uninorms-based approaches. Finally, by the experiment results, we obtain our approaches are feasible, effective, superior to the known three-way decision spaces approaches and have good anti-noise ability. And, the parameter ρ of our approaches is also effective and stable.

A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems

A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems

A behavior three-way decision approach under interval-valued triangular fuzzy numbers with application to the selection of additive manufacturing composites

Additive manufacturing composites, also recognized as three-dimensional (3D) printing composites, are highly anticipated for their potential to replace industrial materials due to the availability of multiple printing processes and optional materials. However, research gaps exist in cognitive deficiencies and psychological behaviors of decision-makers, as well as experimental error effects caused by material testing, resulting in material selection as a challenging issue. Therefore, this study proposes a novel behavior three-way decision model under the interval-valued triangular fuzzy number (IVTFN) to settle the selection issue of 3D printing composites. The research contributions are summarized as follows. First, the IVTFN is presented to account for the impacts of cognitive deficiency and experimental errors, based on which the concepts of information entropy and fuzzy measure are further developed to conduct the criterion weights. In addition, by integrating the prospect theory and regret theory, a framework for constructing the behavioral decision matrix is presented. Moreover, a novel behavior three-way decision model with the perspectives of objective and preference is proposed to classify the decision region. This study presents a comprehensive methodology integrating the three-way decision model and multi-criteria decision-making method to achieve both alternative ranking and alternative classifying. Finally, a research case of 3D printing composites reinforced by continuous hybrid fibers is adopted to illustrate the validity of the methodology. Comparative analysis and sensitivity analysis are also performed. This study offers valuable insights and tools for systematically tackling the 3D printing composite material selection issues.

Three-Way Decisions With Pythagorean Fuzzy TOPSIS for Internet of Drones

Three-Way Decisions With Pythagorean Fuzzy TOPSIS for Internet of Drones

Fuzziness-Based Three-Way Decision With Neighborhood Rough Sets Under the Framework of Shadowed Sets

Fuzziness-Based Three-Way Decision With Neighborhood Rough Sets Under the Framework of Shadowed Sets

Heterogeneous Group Consensus Method With Three-Way Decisions Based on Fuzzy Social Networks and Manipulation Behaviors Under Multiscale Information Systems

Heterogeneous Group Consensus Method With Three-Way Decisions Based on Fuzzy Social Networks and Manipulation Behaviors Under Multiscale Information Systems

GIFTWD: A Prospect Theory-Based Generalized Intuitionistic Fuzzy Three-Way Decision Model

GIFTWD: A Prospect Theory-Based Generalized Intuitionistic Fuzzy Three-Way Decision Model

Models and Algorithms for Optimizing Thresholds in Fuzzy Representation-Based Three-Way Decision

Models and Algorithms for Optimizing Thresholds in Fuzzy Representation-Based Three-Way Decision

A new portfolio approach integrating three-way decision and Encoder–Decoder network

In the stock market, it can be challenging to predict stock trends and identify stocks with investment potential due to uncertainty and risk. Accurate predictive models and reliable asset preselection methods are essential for portfolio management. This paper proposes an integration of the deep learning model, the three-way decision (3WD) theory, and the Mean-Variance (MV) method for asset preselection and optimal asset allocation. In the initial stage, an attention-based Encoder–Decoder model is used to predict asset returns and calculate volatility. It outperforms other benchmark models regarding relative error, absolute error, and directional accuracy. The subsequent stage involves selecting stocks based on expected returns and volatility characteristics. The 3WD theory is then used to identify the delayed decision set for implementing additional investment strategies to hedge portfolio risk and enhance returns. Finally, the MV model is employed for investment portfolio optimization. The proposed model is validated using a substantial dataset from the A-share market’s SSE 50 Index from January 2010 to December 2022. Results indicate that the proposed model outperforms benchmark models in terms of daily average returns, annualized Information ratio, Sortino ratio, and other aspects under certain trading costs. However, further exploration is required to establish a mechanism that limits the quantity of preselected assets to ensure its broad applicability.

A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision

A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision

A dual-channel multimodal sentiment analysis framework based on three-way decision

In the exploration of human–computer interaction, accurately identifying and understanding the user’s sentimental state helps enhance the user experience. Multimodal sentiment analysis aims to identify sentiments through various signals. However, inconsistencies in sentiments between text and visual–audio signals pose a challenge, as models often struggle to capture these differences adequately. To alleviate this issue, we propose a dual-channel sentiment analysis framework based on three-way decision. This framework includes a multimodal model, a visual–audio model, and a three-way decision module. Specifically, the multimodal model and visual–audio model generate predictions, and the three-way decision module selects an appropriate prediction. We improve the binary cross-entropy loss function for the visual–audio model to integrate the three-way decision module better and improve sentiment prediction accuracy. Additionally, we introduce an adversarial training task to ensure differences in predictions between the two models, thereby enhancing the influence of the visual–audio model. Experimental results on four public datasets demonstrate that our framework significantly improves the accuracy of sentiment recognition and reduces the average decision time.