International Journal of Approximate Reasoning最新文献_第9页

Attribute combination reduction in formal concept analysis: A theoretical characterization 形式概念分析中的属性组合约简：一个理论表征

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-06-06 DOI: 10.1016/j.ijar.2025.109498

Qin Zhang , Jianjun Qi , Ling Wei , Siyu Zhao

{"title":"Attribute combination reduction in formal concept analysis: A theoretical characterization","authors":"Qin Zhang , Jianjun Qi , Ling Wei , Siyu Zhao","doi":"10.1016/j.ijar.2025.109498","DOIUrl":"10.1016/j.ijar.2025.109498","url":null,"abstract":"<div><div>Formal concept analysis is an approach relying on hierarchies of formal concepts to acquire knowledge from formal contexts, and is developed on the foundation of lattice theory. In formal concept analysis, reduction theory mainly consists of two types: attribute reduction and concept reduction. The former achieves data reduction but inevitably leads to the loss of the original information in the formal context. The latter involves the deletion of formal concepts while preserving the original information. This paper proposes a new type of reduction called attribute combination reduction to preserve object intents, which leverages the strengths of both attribute reduction and concept reduction while avoiding the limitations of attribute reduction. First, the definition of attribute combination reducts and the judgment theorem of attribute combination consistent sets are given. Then, the relationship between attribute combination reducts and concept reducts is investigated, and the properties of attribute combination reducts are explored. In addition, to narrow the scope for searching attribute combination reducts, a lower bound and an upper bound for their cardinality are provided from the perspectives of the set dimension and the Ferrers dimension of a formal context.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109498"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253444","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

Mixtures of probabilistic logic programs 混合概率逻辑程序

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-06-06 DOI: 10.1016/j.ijar.2025.109497

Damiano Azzolini

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 : 2025-11-01 Epub 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":"2025-11-01","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

Computationally efficient variational-like approximations of possibilistic inferential models 计算效率的似变分的可能性推理模型近似

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-06-13 DOI: 10.1016/j.ijar.2025.109506

Leonardo Cella , Ryan Martin

{"title":"Computationally efficient variational-like approximations of possibilistic inferential models","authors":"Leonardo Cella , Ryan Martin","doi":"10.1016/j.ijar.2025.109506","DOIUrl":"10.1016/j.ijar.2025.109506","url":null,"abstract":"<div><div>Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite the IM framework's theoretical and foundational advantages, efficient computation is a challenge. This paper presents a simple yet powerful numerical strategy for approximating the IM's possibility contour, or at least its <em>α</em>-cut for a specified <span><math><mi>α</mi><mo>∈</mo><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></math></span>. Our proposal starts with the specification of a parametric family that, in a certain sense, approximately covers the credal set associated with the IM's possibility measure. Akin to variational inference, we then propose to tune the parameters of that parametric family so that its <span><math><mn>100</mn><mo>(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo>)</mo><mtext>%</mtext></math></span> credible set roughly matches the IM contour's <em>α</em>-cut. This parametric <em>α</em>-cut matching strategy implies a full approximation to the IM's possibility contour at a fraction of the computational cost associated with previous strategies.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109506"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271892","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

A hybrid machine learning and three-way soft clustering integrated decision-making method with incomplete multi-source heterogeneous attribute information 一种不完全多源异构属性信息的混合机器学习与三向软聚类综合决策方法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-06-04 DOI: 10.1016/j.ijar.2025.109491

Xixuan Zhao , Bingzhen Sun , Xiaodong Chu , Jin Ye , Xiaoli Chu

{"title":"A hybrid machine learning and three-way soft clustering integrated decision-making method with incomplete multi-source heterogeneous attribute information","authors":"Xixuan Zhao , Bingzhen Sun , Xiaodong Chu , Jin Ye , Xiaoli Chu","doi":"10.1016/j.ijar.2025.109491","DOIUrl":"10.1016/j.ijar.2025.109491","url":null,"abstract":"<div><div>Decision-making under uncertainty in the era of big data and technological innovation can often lead to more objective and scientific results. However, data characterized by multi-source heterogeneity, nonlinearity, imbalance, and incompleteness pose a substantial challenge to traditional decision-making theories and methods. In view of this, this paper defines the concept of an incomplete multi-source heterogeneous attribute information system (IMHAS), introduces an attribute reduction method on IMHAS that integrates rough sets and machine learning, and combines three-way soft clustering and hybrid machine learning models. A novel theoretical framework is proposed to address uncertain decision-making problems involving data characterized by multi-source heterogeneity, nonlinearity, imbalance, and incompleteness is proposed. First, IMHAS is established and its attribute reduction method is defined using rough set, neighborhood rough set, bag-of-words, and random forest techniques. Second, to further resolve the correlation between objects in IMHAS, a three-way soft clustering method is introduced. Finally, to target decision-making for different object categories, two types of machine learning models are constructed for handling discrete and conditional decision attributes. The scientific superiority of the proposed method was verified using four distinct datasets from medical and healthcare domains. The results show that the proposed method outperforms all comparative methods, and it can effectively support uncertain decision-making in four different cases. In conclusion, this paper proposes a general theoretical framework for uncertain decision-making based on artificial intelligence techniques for incomplete multi-source heterogeneous data, thus offering realistic guidance for clinical applications involving such data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109491"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230546","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 : 2025-11-01 Epub 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":"2025-11-01","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

Theoretical comparison of Relational Concept Analysis (RCA) and Graph-FCA (GCA) 关系概念分析（RCA）与图形fca （GCA）的理论比较

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-06-05 DOI: 10.1016/j.ijar.2025.109496

Vanessa Fokou , Peggy Cellier , Xavier Dolques , Sébastien Ferré , Florence Le Ber

引用次数: 0

Uncertainty quantification in ordinal classification: A comparison of measures 序数分类中的不确定度量化：测度的比较

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-05-28 DOI: 10.1016/j.ijar.2025.109479

Stefan Haas , Eyke Hüllermeier

{"title":"Uncertainty quantification in ordinal classification: A comparison of measures","authors":"Stefan Haas , Eyke Hüllermeier","doi":"10.1016/j.ijar.2025.109479","DOIUrl":"10.1016/j.ijar.2025.109479","url":null,"abstract":"<div><div>Uncertainty quantification has received increasing attention in machine learning in the recent past, but the focus has mostly been on standard (nominal) classification and regression so far. In this paper, we address the question of how to quantify uncertainty in ordinal classification, where class labels have a natural (linear) order. We reckon that commonly used uncertainty measures such as Shannon entropy, confidence, or margin are not appropriate for the ordinal case. In our search for better measures, we draw inspiration from the social sciences literature, which offers various measures to assess so-called consensus or agreement in ordinal data. We argue that these measures, or, more specifically, the dual measures of dispersion or polarization, do have properties that qualify them as measures of uncertainty. Furthermore, inspired by binary decomposition techniques for multi-class classification in machine learning, we propose a new method that allows for turning any uncertainty measure into an ordinal uncertainty measure in a generic way. We evaluate all measures in an empirical study on twenty-three ordinal benchmark datasets, as well as in a real-world case study on automotive goodwill claim assessment. Our studies confirm that dispersion measures and our binary decomposition method surpass conventional (nominal) uncertainty measures.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109479"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168827","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

Natural revision is contingently-conditionalized revision 自然修正是偶然条件化修正

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-11-01 Epub Date: 2025-06-03 DOI: 10.1016/j.ijar.2025.109489

Paolo Liberatore

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 : 2025-11-01 Epub 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":"2025-11-01","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