International Journal of Approximate Reasoning最新文献_第4页

Generalized conjunction and disjunction of two conditional events in the setting of conditional random quantities 条件随机量下两个条件事件的广义合取与析取

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-28 DOI: 10.1016/j.ijar.2025.109533

Lydia Castronovo , Giuseppe Sanfilippo

{"title":"Generalized conjunction and disjunction of two conditional events in the setting of conditional random quantities","authors":"Lydia Castronovo , Giuseppe Sanfilippo","doi":"10.1016/j.ijar.2025.109533","DOIUrl":"10.1016/j.ijar.2025.109533","url":null,"abstract":"<div><div>In recent papers, notions of conjunction and disjunction of two conditional events as suitable conditional random quantities, which satisfy basic probabilistic properties, have been deepened in the setting of coherence. In this framework, the conjunction and the disjunction of two conditional events are defined as five-valued objects, among which are the values of the (subjectively) assigned probabilities of the two conditional events. In the present paper we propose a generalization of these structures, where these new objects, instead of depending on the probabilities of the two conditional events, depend on two arbitrary values <span><math><mi>a</mi><mo>,</mo><mi>b</mi></math></span> in the unit interval. We show that they are connected by a generalized version of the De Morgan's law and, by means of a geometrical approach, we compute the lower and upper bounds on these new objects both in the precise and the imprecise case. Moreover, some particular cases, obtained for specific values of <em>a</em> and <em>b</em> or in case of some logical relations, are analyzed. The results of this paper lead to the conclusion that the only objects satisfying all the logical and the probabilistic properties already valid for the operations between events are the ones depending on the probabilities of the two conditional events.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109533"},"PeriodicalIF":3.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757942","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

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

Fusing fuzzy rough sets and mean shift for anomaly detection 基于模糊粗糙集和均值移位的异常检测

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-07-18 DOI: 10.1016/j.ijar.2025.109530

Mengyao Liao , Zhiyu Chen , Can Gao , Jie Zhou , Xiaodong Yue

{"title":"Fusing fuzzy rough sets and mean shift for anomaly detection","authors":"Mengyao Liao , Zhiyu Chen , Can Gao , Jie Zhou , Xiaodong Yue","doi":"10.1016/j.ijar.2025.109530","DOIUrl":"10.1016/j.ijar.2025.109530","url":null,"abstract":"<div><div>Outlier detection is a critical but challenging task due to the complex distribution of practical data, and some Fuzzy Rough Sets (FRS)-based methods have been presented to identify outliers from these data. However, these methods still have limitations when facing the co-existence of different types of outliers. In this study, an improved FRS-based unsupervised anomaly detection method is proposed by integrating distance and density information. Specifically, to detect the local outliers, a fuzzy granule density is first defined by introducing a Gaussian kernel similarity to characterize the local density of samples. Then, optimistic and pessimistic fuzzy granule densities are further developed to evaluate the density variation in the local neighborhood. Moreover, a distance measure based on mean shift is introduced to detect global and group outliers. Finally, an outlier detection method that integrates the density and distance measures is designed to effectively identify diverse types of outliers. Extensive experiments on synthetic and public datasets, along with statistical significance analysis, demonstrate the superior performance of the proposed method, achieving an average improvement of at least 12.27% in terms of AUROC.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109530"},"PeriodicalIF":3.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722467","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