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

The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks 随机矩阵的直径：贝叶斯网络灵敏度分析的一种新测度

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-16 DOI: 10.1016/j.ijar.2025.109470

Manuele Leonelli , Jim Q. Smith

{"title":"The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks","authors":"Manuele Leonelli , Jim Q. Smith","doi":"10.1016/j.ijar.2025.109470","DOIUrl":"10.1016/j.ijar.2025.109470","url":null,"abstract":"<div><div>Bayesian networks are one of the most widely used classes of probabilistic models for risk management and decision support because of their interpretability and flexibility in including heterogeneous pieces of information. In any applied modelling, it is critical to assess how robust the inferences on certain target variables are to changes in the model. In Bayesian networks, these analyses fall under the umbrella of sensitivity analysis, which is most commonly carried out by quantifying dissimilarities using Kullback-Leibler information measures. We argue that robustness methods based instead on the total variation distance provide simple and more valuable bounds on robustness to misspecification, which are both formally justifiable and transparent. We introduce a novel measure of dependence in conditional probability tables called the <em>diameter</em> to derive such bounds. This measure quantifies the strength of dependence between a variable and its parents. Furthermore, the diameter is a versatile measure that can be applied to a wide range of sensitivity analysis tasks. It is particularly useful for quantifying edge strength, assessing influence between pairs of variables, detecting asymmetric dependence, and amalgamating levels of variables. This flexibility makes the diameter an invaluable tool for enhancing the robustness and interpretability of Bayesian network models in applied risk management and decision support.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"185 ","pages":"Article 109470"},"PeriodicalIF":3.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089915","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 comparative analysis of aggregation rules for coherent lower previsions 连贯较低经费汇总规则的比较分析

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-16 DOI: 10.1016/j.ijar.2025.109474

Enrique Miranda, Juan J. Salamanca, Ignacio Montes

引用次数: 0

The structure of rough sets defined by reflexive relations 由自反关系定义的粗糙集的结构

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-16 DOI: 10.1016/j.ijar.2025.109471

Jouni Järvinen , Sándor Radeleczki

引用次数: 0

Information fusion based conflict analysis model for multi-source fuzzy data 基于信息融合的多源模糊数据冲突分析模型

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-15 DOI: 10.1016/j.ijar.2025.109472

Xinxin Tang , Mengyu Yan , Jinhai Li , Fei Hao

{"title":"Information fusion based conflict analysis model for multi-source fuzzy data","authors":"Xinxin Tang , Mengyu Yan , Jinhai Li , Fei Hao","doi":"10.1016/j.ijar.2025.109472","DOIUrl":"10.1016/j.ijar.2025.109472","url":null,"abstract":"<div><div>Conflict is ubiquitous in life. Conflict analysis is a tool for understanding conflicts, whose aim is to analyze the conflict situations in data to help decision makers avoid risks. Existing conflict analysis methods mainly focus on single-source data. However, the emergence of big data era has generated more complex data, such as multi-source data obtained from different perspectives, which can capture details that single-source data is missing. Not only that, most data also exhibit characteristics of fuzziness. The above situations make it more challenging to construct a conflict analysis model in the environment of multi-source fuzzy data to acquire a compliant decision. Therefore, conflict analysis for multi-source fuzzy data is a worthy research topic. However, the existing few studies on multi-source fuzzy data either favor attribute values or ignore conflict resolution, which reduces the conflict resolution performance due to underutilizing attribute information. To solve the above problem, we divide the attribute values of multi-source fuzzy data into three attitude intervals to distinguish different attitudes of agents. Then, we propose a function to measure conflict and construct a conflict analysis model for a multi-source fuzzy formal context. Additionally, we put forward an information fusion method based on the minimum of fuzzy entropy, whose purpose is to achieve conflict resolution quickly. Finally, experiments conducted on 18 datasets demonstrate that our information fusion method can achieve conflict resolution effectively, and provide a useful reference for decision-makers.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"185 ","pages":"Article 109472"},"PeriodicalIF":3.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083735","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

Granular-ball fuzzy information-based outlier detector 基于模糊信息的颗粒球离群值检测器

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-15 DOI: 10.1016/j.ijar.2025.109473

Qilin Li , Zhong Yuan , Dezhong Peng , Xiaomin Song , Huiming Zheng , Xinyu Su

{"title":"Granular-ball fuzzy information-based outlier detector","authors":"Qilin Li , Zhong Yuan , Dezhong Peng , Xiaomin Song , Huiming Zheng , Xinyu Su","doi":"10.1016/j.ijar.2025.109473","DOIUrl":"10.1016/j.ijar.2025.109473","url":null,"abstract":"<div><div>Outlier detection is an important part of the process of carrying out data mining and analysis and has been applied to many fields. Existing methods are typically anchored in a single-sample processing paradigm, where the processing unit is each individual and single-granularity sample. This processing paradigm is inefficient and ignores the multi-granularity features inherent in data. In addition, these methods often overlook the uncertainty information present in the data. To remedy the above-mentioned shortcomings, we propose an unsupervised outlier detection method based on Granular-Ball Fuzzy Granules (GBFG). GBFG adopts a granular-ball-based computing paradigm, where the fundamental processing units are granular-balls. This shift from individual samples to granular-balls enables GBFG to capture the overall data structure from a multi-granularity perspective and improve the performance of outlier detection. Subsequently, we calculate the outlier factor based on the outlier degrees of the granular-ball fuzzy granules to which the sample belongs, serving as a measure of the outlier degrees of samples. The experimental results prove that GBFG has a remarkable performance compared with the existing excellent algorithms. The code of GBFG is publicly available on <span><span>https://github.com/Mxeron/GBFG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"185 ","pages":"Article 109473"},"PeriodicalIF":3.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089914","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

Latent Gaussian and Hüsler–Reiss graphical models with Golazo penalty 具有Golazo惩罚的潜在高斯和h<s:1> sler - reiss图形模型

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-14 DOI: 10.1016/j.ijar.2025.109468

Ignacio Echave-Sustaeta Rodríguez, Frank Röttger

{"title":"Latent Gaussian and Hüsler–Reiss graphical models with Golazo penalty","authors":"Ignacio Echave-Sustaeta Rodríguez, Frank Röttger","doi":"10.1016/j.ijar.2025.109468","DOIUrl":"10.1016/j.ijar.2025.109468","url":null,"abstract":"<div><div>The existence of latent variables in practical problems is common, for example when some variables are difficult or expensive to measure, or simply unknown. When latent variables are unaccounted for, structure learning for Gaussian graphical models can be blurred by additional correlation between the observed variables that is incurred by the latent variables. A standard approach for this problem is a latent version of the graphical lasso that splits the inverse covariance matrix into a sparse and a low-rank part that are penalized separately. This approach has recently been extended successfully to Hüsler–Reiss graphical models, which can be considered as an analogue of Gaussian graphical models in extreme value statistics. In this paper we propose a generalization of structure learning for Gaussian and Hüsler–Reiss graphical models via the flexible Golazo penalty. This allows us to introduce latent versions of for example the adaptive lasso, positive dependence constraints or predetermined sparsity patterns, and combinations of those. We develop algorithms for both latent graphical models with the Golazo penalty and demonstrate it on simulated and real data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"185 ","pages":"Article 109468"},"PeriodicalIF":3.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089912","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 novel multi-label feature selection method based on conditional entropy and its acceleration mechanism 一种新的基于条件熵的多标签特征选择方法及其加速机制

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-13 DOI: 10.1016/j.ijar.2025.109469

Chengwei Liao, Bin Yang

{"title":"A novel multi-label feature selection method based on conditional entropy and its acceleration mechanism","authors":"Chengwei Liao, Bin Yang","doi":"10.1016/j.ijar.2025.109469","DOIUrl":"10.1016/j.ijar.2025.109469","url":null,"abstract":"<div><div>In multi-label learning, feature selection is a crucial step for enhancing model performance and reducing computational complexity. However, due to the interdependence among labels and the high dimensionality of feature sets, traditional single-label feature selection methods often underperform in multi-label scenarios. Moreover, many existing feature selection methods typically require a comprehensive evaluation of all features and samples in each iteration, resulting in high computational complexity. To address this issue, this paper proposes a feature selection algorithm based on fuzzy conditional entropy within the framework of fuzzy rough set theory. The method gradually identifies optimal features through iterative optimization and systematically filters out features and samples that do not contribute to the current feature subset. Specifically, the filtered features and samples are incorporated into redundant feature and sample sets, thereby dynamically excluding these redundant elements in subsequent iterations and avoiding unnecessary computations. Experiments conducted on 10 multi-label datasets demonstrate that the proposed algorithm outperforms eight other methods in terms of performance.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"185 ","pages":"Article 109469"},"PeriodicalIF":3.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069068","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

Bel coalitional games with application to market games 联合博弈及其在市场博弈中的应用

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-09 DOI: 10.1016/j.ijar.2025.109466

Michel Grabisch , Silvia Lorenzini

引用次数: 0

Graded hexagon of opposition in fuzzy natural logic with new intermediate quantifiers 具有新中间量词的模糊自然逻辑中对立的分级六边形

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-08 DOI: 10.1016/j.ijar.2025.109465

Petra Murinová , Karel Fiala , Stefania Boffa , Vilém Novák

引用次数: 0

Three-way space structure and clustering of categorical data 三向空间结构与分类数据聚类

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-05 DOI: 10.1016/j.ijar.2025.109457

Ruxiao Zhang , Hongying Zhang , Yuhua Qian

{"title":"Three-way space structure and clustering of categorical data","authors":"Ruxiao Zhang , Hongying Zhang , Yuhua Qian","doi":"10.1016/j.ijar.2025.109457","DOIUrl":"10.1016/j.ijar.2025.109457","url":null,"abstract":"<div><div>Measures of similarity and dissimilarity play a pivotal role in discovering the structural characteristics of categorical data, which is crucial in various fields and applications because many real-world problems involve qualitative information. However, co-occurrence probability, a popular similarity measure widely used to construct a linear representation space for analyzing categorical data, has been verified to be inefficient in describing the global similarity of objects. In this paper, drawing inspiration from three-way concept theory, a novel three-way representation scheme is developed through the tripartite definition of <em>Common Feature Values (CV), Non-common Feature Values (NV)</em>, and <em>Respective Feature Values (RV)</em>, which enables comprehensive characterization of intrinsic feature relationships while maintaining interpretative granularity. These three concepts correspond to the positive, negative, and boundary domains of the feature universe, respectively, which map a set of categorical objects into three Euclidean spaces. Utilizing the three-way categorical data representation scheme, we develop a unified framework for three-way space structure based categorical clustering algorithms (TWSBC). Finally, to verify the performance of the TWSBC algorithm, which combines the representation scheme with the k-means algorithm, we employ the SBC- type algorithm as a reference. Extensive experiments show that our proposed TWSBC-type algorithms distinctly outperform SBC-type methods in terms of space correlation and clustering performance.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"184 ","pages":"Article 109457"},"PeriodicalIF":3.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089487","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