International Journal of Approximate Reasoning最新文献

Knowledge granularity reduction for fuzzy relation decision systems 模糊关系决策系统的知识粒度约简

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-06-04 DOI: 10.1016/j.ijar.2025.109494

Qing Wang , Xiuwei Gao , Guilong Liu

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 : 2025-06-04 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":"2025-06-04","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

Bayesianize fuzziness in the statistical analysis of fuzzy data 贝叶斯模糊化在模糊数据统计分析中的应用

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-06-04 DOI: 10.1016/j.ijar.2025.109495

Antonio Calcagnì , Przemysław Grzegorzewski , Maciej Romaniuk

{"title":"Bayesianize fuzziness in the statistical analysis of fuzzy data","authors":"Antonio Calcagnì , Przemysław Grzegorzewski , Maciej Romaniuk","doi":"10.1016/j.ijar.2025.109495","DOIUrl":"10.1016/j.ijar.2025.109495","url":null,"abstract":"<div><div>Fuzzy data, prevalent in social sciences and other fields, capture uncertainties arising from subjective evaluations and measurement imprecision. Despite significant advancements in fuzzy statistics, a unified inferential regression-based framework remains undeveloped. Hence, we propose a novel approach for analyzing bounded fuzzy variables within a regression framework. Building on the premise that fuzzy data result from a process analogous to statistical coarsening, we introduce a conditional probabilistic approach that links observed fuzzy statistics (e.g., mode, spread) to the underlying, unobserved statistical model, which depends on external covariates. The inferential problem is addressed using Approximate Bayesian methods, mainly through a Gibbs sampler incorporating a quadratic approximation of the posterior distribution. Simulation studies and applications involving external validations are employed to evaluate the effectiveness of the proposed approach for fuzzy data analysis. By reintegrating fuzzy data analysis into a more traditional statistical framework, this work provides a significant step toward enhancing the interpretability and applicability of fuzzy statistical methods in many applicative contexts.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109495"},"PeriodicalIF":3.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230545","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

Three-way conflict analysis model via the best-worst method: Balancing subjective preferences and objective data on incomplete and dispersed systems 基于最佳-最差方法的三方冲突分析模型：在不完整和分散的系统中平衡主观偏好和客观数据

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-06-03 DOI: 10.1016/j.ijar.2025.109490

Junjie Zhu , Qinghua Zhang , Nanfang Luo , Fan Liu , Longjun Yin

{"title":"Three-way conflict analysis model via the best-worst method: Balancing subjective preferences and objective data on incomplete and dispersed systems","authors":"Junjie Zhu , Qinghua Zhang , Nanfang Luo , Fan Liu , Longjun Yin","doi":"10.1016/j.ijar.2025.109490","DOIUrl":"10.1016/j.ijar.2025.109490","url":null,"abstract":"<div><div>In real-world environments, different issues contribute to conflicts with varying weights. However, current conflict analysis weighting models face significant limitations when dealing with incomplete data and dispersed knowledge. Objective methods are sensitive to missing values and struggle to accurately capture authentic preferences, while subjective approaches lack systematic evaluation criteria, leading to substantial randomness in weight assignments. Therefore, a three-way conflict analysis model via the best-worst method is proposed, which is combined with a correlation coefficient method to balance subjective preferences and objective data. First, the trisection of agent pairs is derived through Bayesian minimum risk. Subsequently, a new conflict distance function is defined on the incomplete information system to provide a more precise measurement of conflict degrees. Then, for incomplete and dispersed information systems, a maximal coalition-based agent partitioning algorithm is designed, along with a new weighted voting mechanism to aggregate dispersed knowledge. Finally, the scientific transparency of the weighting process, as well as the robustness and feasibility of the model, are demonstrated through experimental analysis.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109490"},"PeriodicalIF":3.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221952","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 : 2025-06-03 DOI: 10.1016/j.ijar.2025.109489

Paolo Liberatore

引用次数: 0

Predicting graphical models based on functional data 基于功能数据的预测图形模型

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-06-03 DOI: 10.1016/j.ijar.2025.109493

Qiying Wu , Huiwen Wang

{"title":"Predicting graphical models based on functional data","authors":"Qiying Wu , Huiwen Wang","doi":"10.1016/j.ijar.2025.109493","DOIUrl":"10.1016/j.ijar.2025.109493","url":null,"abstract":"<div><div>Graphical models are widely used to model complex relationships between variables in various fields. However, existing analysis methods focus primarily on scalar data and give little attention to addressing the challenges posed by nonscalar data, e.g., functional data, which are prevalent in many real-world applications. Additionally, most methods assume a static graphical model within the observed period, neglecting the dynamic changes that may occur over time. In this paper, we propose a novel method for predicting graphical models based on functional data. Our approach transforms functional data into finite-dimensional vectors via basis function expansion and cross-validation. We then establish a sequential prediction model for determining the correlation coefficient matrix of the decomposed data, thus accounting for the constraints imposed on the matrix via a transformation technique. Finally, we employ conditional independence tests to identify the edges of the predicted graphical model. We demonstrate the effectiveness of our method through extensive simulations and real data analyses. The results show that our method performs better than the competing methods in terms of prediction accuracy and provides valuable insights into the dynamic changes exhibited by a network. This work opens new possibilities for conducting graphical model analyses in various domains, particularly in terms of handling functional data and predicting dynamic relationships.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109493"},"PeriodicalIF":3.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212380","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

The language of Contextual Attribute Logics – Introduction and survey 语境属性逻辑语言——介绍与综述

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-30 DOI: 10.1016/j.ijar.2025.109487

Bernhard Ganter

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 : 2025-05-28 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":"2025-05-28","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

Algorithms for computing the set of acceptable arguments 用于计算可接受参数集的算法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-26 DOI: 10.1016/j.ijar.2025.109478

Lars Bengel , Matthias Thimm , Federico Cerutti , Mauro Vallati

引用次数: 0

Corrigendum to “Estimating bounds on causal effects in high-dimensional and possibly confounded systems” [Int. J. Approx. Reason. 88 (2017) 371–384] “估计高维和可能混杂的系统中因果效应的界限”的勘误表[Int.]j .约。原因。88 (2017)371-384]

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-22 DOI: 10.1016/j.ijar.2025.109475

Daniel Malinsky , Peter Spirtes

引用次数: 0