International Journal of Approximate Reasoning最新文献

Explainable multi-criteria decision-making: A three-way decision perspective 可解释的多准则决策：三向决策视角

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-18 DOI: 10.1016/j.ijar.2025.109528

Chengjun Shi, Yiyu Yao

{"title":"Explainable multi-criteria decision-making: A three-way decision perspective","authors":"Chengjun Shi, Yiyu Yao","doi":"10.1016/j.ijar.2025.109528","DOIUrl":"10.1016/j.ijar.2025.109528","url":null,"abstract":"<div><div>This paper proposes an Explainable Multi-Criteria Decision-Making (XMCDM) framework that constructs trilevel explanations with respect to classic multi-criteria decision-making methods. The framework consists of explainable data preparation, explainable decision analysis, and explainable decision support, which integrates ideas from three-way decision and symbols-meaning-value spaces. First, we briefly introduce the key concepts at each level and list potential issues to be resolved, including gathering multi-criteria data, interpreting multi-criteria decision-making working principles, and offering effective outcome presentation. We examine existing literature that solves part of those questions and point out that rule-based explanations may be applicable and efficient to explain ranking/ordering results. Then, we discuss two methods that generate three-way rankings with respect to an individual criterion and integrate three-way rankings with multi-criteria ranking. We modify the Iterative Dichotomiser 3 algorithm to build rule-based explanations. Finally, after giving a small illustrative example, we design experiments on five real-life datasets, test explainability of three classic multi-criteria decision-making methods, and tune the thresholds. The experimental results demonstrate that our proposed framework is feasible and adaptable to various data characteristics.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109528"},"PeriodicalIF":3.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximal consistent blocks-based optimistic and pessimistic probabilistic rough fuzzy sets and their applications in three-way multiple attribute decision-making 基于最大一致块的乐观和悲观概率粗糙模糊集及其在三向多属性决策中的应用

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-17 DOI: 10.1016/j.ijar.2025.109529

Yan Sun , Bin Pang , Ju-Sheng Mi , Wei-Zhi Wu

{"title":"Maximal consistent blocks-based optimistic and pessimistic probabilistic rough fuzzy sets and their applications in three-way multiple attribute decision-making","authors":"Yan Sun , Bin Pang , Ju-Sheng Mi , Wei-Zhi Wu","doi":"10.1016/j.ijar.2025.109529","DOIUrl":"10.1016/j.ijar.2025.109529","url":null,"abstract":"<div><div>The integration of three-way decision (3WD) into multiple attribute decision-making (MADM) problems has emerged as a pivotal research area. 3WD can effectively manage the inherent uncertainty within the decision-making process. Additionally, it offers a semantic interpretation of the outcomes. In this paper, we introduce two innovative 3WD-MADM approaches, with a focus on granule selection and the handling of multi-type information in the framework of three-way decisions. Firstly, we construct maximal consistent blocks (MCBs)-based pessimistic and optimistic probabilistic rough fuzzy set (RFS) models and investigate their properties to ascertain their efficacy and reliability in decision-making contexts. Then, we define relative loss functions associated with “good state” and “bad state” scenarios. Building on this, we introduce four types of 3WDs based on our newly proposed optimistic and pessimistic probabilistic RFSs. Furthermore, we integrate the 3WDs information from both scenarios to formulate optimistic and pessimistic 3WD-MADM approaches, handling both single-valued fuzzy and intuitionistic fuzzy information. Finally, we contrast our proposed methodologies with the current MADM methods, and demonstrate their validity, significance and generalization ability.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109529"},"PeriodicalIF":3.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Centralized ordered weighted averaging operator weights and their properties 集中有序加权平均算子权值及其性质

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-16 DOI: 10.1016/j.ijar.2025.109477

Byeong Seok Ahn

{"title":"Centralized ordered weighted averaging operator weights and their properties","authors":"Byeong Seok Ahn","doi":"10.1016/j.ijar.2025.109477","DOIUrl":"10.1016/j.ijar.2025.109477","url":null,"abstract":"<div><div>We propose a method for generating ordered weighted averaging (OWA) operator weights based on a preference order expressed through a set of inequalities representing the relative importance of criteria. The resulting centralized OWA (COWA) operator weights are: (i) computationally derived by averaging the coordinates of the extreme points of the feasible set; (ii) mathematically defined as the weights that minimize the sum of squared deviations from each extreme point; (iii) geometrically located at the center of the feasible region defined by the inequalities.</div><div>Moreover, for several sets of inequalities, the COWA operator weights closely resemble those of the maximum entropy OWA operator and consistently exhibit a constant attitudinal character (<em>AC</em>), regardless of the number of criteria.</div><div>For validation purposes, we introduce a method for generating COWA operator weights that satisfy a specified <em>AC</em>, and demonstrate their similarity to the maximum entropy OWA operator weights through sample tests with varying number of criteria and <em>AC</em> values. The strength of the preference order associated with a specific <em>AC</em> provides deeper insight into how the <em>AC</em> relates to the ordinal relationships among the criteria.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109477"},"PeriodicalIF":3.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy sequential three-way multi-scale attribute decision-making method based on individual risk attitude and multi classification features 基于个体风险态度和多分类特征的模糊序列三尺度属性决策方法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-15 DOI: 10.1016/j.ijar.2025.109525

Jin Qian , Yuehua Lu , Ying Yu , Di Wang

{"title":"Fuzzy sequential three-way multi-scale attribute decision-making method based on individual risk attitude and multi classification features","authors":"Jin Qian , Yuehua Lu , Ying Yu , Di Wang","doi":"10.1016/j.ijar.2025.109525","DOIUrl":"10.1016/j.ijar.2025.109525","url":null,"abstract":"<div><div>Multi-attribute decision-making research is of great significance for solving macro problems. However, the existing multi-attribute decision-making methods face two problems: one is how to comprehensively consider the impact of irrational behavior on the decision-making results; the other is how to make intelligent decisions on the evaluation information of “multi-level, multi-classification, multi-perspective”. To address the above two issues, this paper establishes a fuzzy sequential three-way multi-scale attribute decision-making method based on individual risk attitudes and multi-classification features. First, we construct multiple attribute combinations from the inconsistent multi-scale attribute set and weight and aggregate them into comprehensive decision attributes, thereby transforming them from multi-scale to multi-view. Next, we identify multiple attribute clusters through hierarchical clustering and create a class-cluster dependency definition to determine the sequential set using a heuristic algorithm. We then propose a specific sequential three-way decision model within the framework of granular computing, tailored to the characteristics of the evaluation information. For object ranking, we pre-rank the objects based on regret theory and develop two methods to determine category weights based on the classification results obtained from the three-way decision. The stability and effectiveness of the proposed method are verified through corresponding experiments and comparative analysis of real cases.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109525"},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cooperative games with fuzzy characteristic functions on concept lattices 概念格上具有模糊特征函数的合作对策

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-14 DOI: 10.1016/j.ijar.2025.109527

Martin Waffo Kemgne , Blaise Bleriot Koguep Njionou , Dmitry I. Ignatov , Leonard Kwuida

引用次数: 0

Cut-elimination theorems for some logics associated with double Stone algebras 双斯通代数相关逻辑的切消定理

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-09 DOI: 10.1016/j.ijar.2025.109526

Martín Figallo, Juan S. Slagter

引用次数: 0

Stable structure learning with HC-Stable and Tabu-Stable algorithms HC-Stable和Tabu-Stable算法的稳定结构学习

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-07 DOI: 10.1016/j.ijar.2025.109522

Neville K. Kitson, Anthony C. Constantinou

{"title":"Stable structure learning with HC-Stable and Tabu-Stable algorithms","authors":"Neville K. Kitson, Anthony C. Constantinou","doi":"10.1016/j.ijar.2025.109522","DOIUrl":"10.1016/j.ijar.2025.109522","url":null,"abstract":"<div><div>Many Bayesian Network structure learning algorithms are unstable, with the learned graph sensitive to arbitrary dataset artifacts, such as the ordering of columns (i.e., variable order). PC-Stable <span><span>[1]</span></span> attempts to address this issue for the widely-used PC algorithm, prompting researchers to use the ‘stable’ version instead. However, this problem seems to have been overlooked for score-based algorithms. In this study, we show that some widely-used score-based algorithms, as well as hybrid and constraint-based algorithms, including PC-Stable, suffer from the same issue. We propose a novel solution for score-based greedy hill-climbing that eliminates instability by determining a stable node order, leading to consistent results regardless of variable ordering. The new Tabu-Stable algorithms achieve the highest overall performance in terms of mean BIC score, log-likelihood, and structural accuracy across networks. These results highlight the importance of addressing instability in structure learning and provide a robust and practical approach for future applications. This paper extends the scope and impact of our previous work presented at Probabilistic Graphical Models 2024 <span><span>[2]</span></span> by incorporating continuous variables, implementing new stable orders that improve performance further, and demonstrating that the approach remains effective in the presence of sampling noise. The implementations, along with usage instructions, are freely available on GitHub at <span><span>https://github.com/causal-iq/discovery</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109522"},"PeriodicalIF":3.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chain graphs structure learning given local background knowledge 给定局部背景知识的链图结构学习

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-05 DOI: 10.1016/j.ijar.2025.109524

Shujing Yang , Fuyuan Cao , Kui Yu , Jiye Liang

{"title":"Chain graphs structure learning given local background knowledge","authors":"Shujing Yang , Fuyuan Cao , Kui Yu , Jiye Liang","doi":"10.1016/j.ijar.2025.109524","DOIUrl":"10.1016/j.ijar.2025.109524","url":null,"abstract":"<div><div>Chain graphs structure learning aims to identify and infer causal relations and symmetric association relations between variables in data. However, existing chain graphs structure learning algorithms cannot uniquely determine the causal relations from data among some variables due to the independence of these variables corresponding to multiple structures, making them only learn Markov equivalence classes of chain graphs. To alleviate this issue, we propose a <strong>C</strong>hain <strong>G</strong>raphs structure <strong>L</strong>earning algorithm <strong>G</strong>iven local background <strong>K</strong>nowledge (CGLGK). CGLGK initially learns the adjacencies and spouses of variables, constructs the skeleton of chain graphs using the adjacencies, corrects the connections between variables in the skeleton guided by local background knowledge, and orients the edges using the adjacencies and spouses to obtain the Markov equivalence classes of chain graphs. Next, CGLGK fuses local background knowledge with the learned Markov equivalence classes to obtain new knowledge. Finally, it utilizes the local valid orientation rule to orient edges within the Markov equivalence classes based on the updated knowledge, resulting in the final chain graphs structure. Meanwhile, we conducted the theoretical analysis to prove the correctness of CGLGK, and its effectiveness is verified by comparison with the classical and state-of-the-art algorithms on synthetic and real data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109524"},"PeriodicalIF":3.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Divide and conquer for causal computation 分治法用于因果计算

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-05 DOI: 10.1016/j.ijar.2025.109520

Anna Rodum Bjøru , Rafael Cabañas , Helge Langseth , Antonio Salmerón

{"title":"Divide and conquer for causal computation","authors":"Anna Rodum Bjøru , Rafael Cabañas , Helge Langseth , Antonio Salmerón","doi":"10.1016/j.ijar.2025.109520","DOIUrl":"10.1016/j.ijar.2025.109520","url":null,"abstract":"<div><div>Structural causal models are a powerful framework for causal and counterfactual inference, extending the capabilities of traditional Bayesian networks. These models comprise endogenous and exogenous variables, where the exogenous variables frequently lack clear semantic interpretation. Exogenous variables are typically unobservable, rendering certain counterfactual queries unidentifiable. In such cases, standard inference algorithms for Bayesian networks are insufficient. Recent methods attempt to bound unidentifiable queries through imprecise estimation of exogenous probabilities. However, these methods become computationally infeasible as the cardinality of the exogenous variables increases, thereby constraining the complexity of applicable models. In this paper we study a divide-and-conquer approach that decomposes a general causal model into a set of submodels with low-cardinality exogenous variables, enabling exact calculation of any query within these submodels. By aggregating results from the submodels, efficient approximations of bounds for queries in the original model are obtained. Our proposal is able to handle models with variables of any cardinality assuming that there are no unobserved confounders. We show that the method is theoretically robust, and experimental results demonstrate that it achieves more accurate bounds with lower computational costs compared to existing techniques.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109520"},"PeriodicalIF":3.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FDACNet: Enhancing time-series classification with fuzzy feature and integrated self-attention and temporal convolution FDACNet：利用模糊特征，结合自关注和时间卷积增强时间序列分类

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-02 DOI: 10.1016/j.ijar.2025.109521

Xiuwei Chen, Li Lai, Maokang Luo

{"title":"FDACNet: Enhancing time-series classification with fuzzy feature and integrated self-attention and temporal convolution","authors":"Xiuwei Chen, Li Lai, Maokang Luo","doi":"10.1016/j.ijar.2025.109521","DOIUrl":"10.1016/j.ijar.2025.109521","url":null,"abstract":"<div><div>Time-series classification is crucial in time series analysis and holds significant importance in real-world scenarios. Applying self-attention and temporal convolution techniques is paramount when dealing with time series data. The self-attention mechanism enables the capture of correlations between different time steps in a sequence, thereby facilitating the handling of long-term dependencies. Meanwhile, temporal convolution is designed explicitly for processing time series data, effectively capturing temporal dependencies through convolutional layers. The integration of the two technologies plays a pivotal role in time series analysis, enabling accurate temporal classification. This paper proposes a novel net with fuzzy features and integrated self-attention and temporal convolution, denoted as FDACNet. The proposed net introduces two key components: FD-FE for fuzzy dominated feature extraction, and ATCmix for integrating self-attention and temporal convolution. FD-FE captures trend information by defining gradient relationship between time points within a time series sample. On the other hand, ATCmix combines convolution and self-attention to reduce parameters and enhance efficiency in handling time-series data. Finally, the proposed method is evaluated on twenty datasets and compared against twelve other state-of-the-art approaches. Experimental results demonstrate the superior classification accuracy of the proposed model, showcasing a 5.2% and 7.1% enhancement in average accuracy compared to the state-of-the-art convolution-based and transformer-based methods ModernTCN and iTransformer.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109521"},"PeriodicalIF":3.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0