International Journal of Approximate Reasoning最新文献

Optimal scale combination selection based on a monotonic variable precision multi-scale rough set model 基于单调变精度多尺度粗糙集模型的最优尺度组合选择

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-10 DOI: 10.1016/j.ijar.2025.109569

Ruili Guo , Qinghua Zhang , Yunlong Cheng , Ying Yang , Hang Zhong

{"title":"Optimal scale combination selection based on a monotonic variable precision multi-scale rough set model","authors":"Ruili Guo , Qinghua Zhang , Yunlong Cheng , Ying Yang , Hang Zhong","doi":"10.1016/j.ijar.2025.109569","DOIUrl":"10.1016/j.ijar.2025.109569","url":null,"abstract":"<div><div>Most existing generalized multi-scale rough set models (GMRSMs) are based on Pawlak's rough set, which lacks fault tolerance and thus limits their generalization ability. To improve generalization, the variable precision generalized multi-scale rough set model (VPGMRSM) was proposed. However, this model disrupts the monotonicity of the positive region, posing challenges for optimal scale combination (OSC) selection. To address these issues, a monotonic VPGMRSM is proposed in this paper through a two-stage approximation process. The proposed model preserves the monotonicity of the GMRSM and the fault tolerance of the VPGMRSM, and is further applied to OSC selection. First, the non-monotonicity of the positive region in the original VPGMRSM is analyzed. Then, a monotonic VPGMRSM is proposed, whose information measurements are proven to satisfy the monotonicity lacking in the original model. Second, an extended definition of OSC is proposed based on the positive region in the new model, which significantly simplifies and improves the efficiency of the OSC selection process. Third, two OSC selection algorithms are proposed: one based on binary search to find a single OSC, and the other based on three-way decision theory to identify all OSCs. Finally, the experimental results validate the monotonicity of the positive region in the new model and demonstrate that the proposed algorithms are not only suitable for VPGMRSMs, but also effectively reduce the computation time.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109569"},"PeriodicalIF":3.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044515","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

Unimodular triangulations in Łukasiewicz logic: Complexity bounds of probabilistic coherence Łukasiewicz逻辑中的单模三角剖分：概率相干性的复杂度界限

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-09 DOI: 10.1016/j.ijar.2025.109565

Tommaso Flaminio , Serafina Lapenta , Sebastiano Napolitano

引用次数: 0

Some fuzzy neighborhood operators on fuzzy β-covering approximation space and their application in user preference evaluation 模糊β覆盖近似空间上的模糊邻域算子及其在用户偏好评价中的应用

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-08 DOI: 10.1016/j.ijar.2025.109566

Wei Li , Xiaolei Wang , Bin Yang

{"title":"Some fuzzy neighborhood operators on fuzzy β-covering approximation space and their application in user preference evaluation","authors":"Wei Li , Xiaolei Wang , Bin Yang","doi":"10.1016/j.ijar.2025.109566","DOIUrl":"10.1016/j.ijar.2025.109566","url":null,"abstract":"<div><div>As a generalization of covering, fuzzy <em>β</em>-covering provides a more accurate and practical representation for incomplete information. This paper primarily proposes several fuzzy neighborhood operators based on diverse aggregation functions in an fuzzy <em>β</em>-covering approximation space (F<em>β</em>CAS) and develops a novel TOPSIS method to address the decision-making problem related to user preference factors. First, two classes of fuzzy neighborhood operators are introduced, derived from <em>t</em>-norms, overlap functions and their residual implications in an F<em>β</em>CAS, with their properties thoroughly analyzed. In addition, multiple fuzzy <em>β</em>-coverings are generated from the original fuzzy <em>β</em>-covering, and the classifications of fuzzy neighborhood operators, along with their partial order relationships, are examined. Based on these operators, two kinds of fuzzy <em>β</em>-covering-based rough sets (F<em>β</em>CRS) are established. Finally, an F<em>β</em>CRS-based fuzzy TOPSIS method is developed to evaluate user preference factors for fresh fruit, thereby demonstrating the rationality and feasibility of the proposed approach.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109566"},"PeriodicalIF":3.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044513","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 framework for trust network analysis: Connectivity-based intuitionistic fuzzy rough digraph 一种新的信任网络分析框架：基于连通性的直觉模糊粗有向图

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-05 DOI: 10.1016/j.ijar.2025.109564

Danyang Wang , Ping Zhu

{"title":"A novel framework for trust network analysis: Connectivity-based intuitionistic fuzzy rough digraph","authors":"Danyang Wang , Ping Zhu","doi":"10.1016/j.ijar.2025.109564","DOIUrl":"10.1016/j.ijar.2025.109564","url":null,"abstract":"<div><div>Network connectivity analysis enables information source tracing and spread regulation in social systems. While existing studies have explored intuitionistic fuzzy rough (IFR) digraphs to address the representation needs of pervasive uncertainties and dual-polarity information in real-world networks, their neglect of connectivity characteristics has limited applicability in information diffusion scenarios. This study breaks through conventional framework and proposes a connectivity-based IFR digraph model, which achieves comprehensive representation of information oppositionality, uncertainty, and propagative characteristic. First, we explore minimum equivalent intuitionistic fuzzy subgraph (MEIFS) and semi-maximum equivalent intuitionistic fuzzy supergraph (SEIFS). MEIFS preserves original strength of connectedness through minimal arc sets, while SEIFS achieves the same objective via redundant arc augmentation. This complementarity provides a mathematical tool for approximating complex networks. Then, a connectivity-based IFR digraph model is established through the synergy of MEIFS and SEIFS. Finally, according to the co-occurrence characteristics of trust and distrust in society, the community detection algorithm and multi-core-node mining method for IFR trust networks are developed. Comparative analysis with three existing methods demonstrates the superiority of the proposed technique in approximate modeling of adversarial information propagation systems.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109564"},"PeriodicalIF":3.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019227","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

Special issue on the Twelfth International Conference on Probabilistic Graphical Models (PGM 2024) 第十二届国际概率图模型会议特刊（PGM 2024）

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-05 DOI: 10.1016/j.ijar.2025.109571

Silja Renooij, Johan Kwisthout, Janneke H. Bolt

引用次数: 0

Superhedging supermartingales Superhedging上鞅

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-03 DOI: 10.1016/j.ijar.2025.109567

C. Bender , S.E. Ferrando , K. Gajewski , A.L. González

引用次数: 0

On the optimality of coin-betting for mean estimation 基于均值估计的投币最优性研究

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-09-01 DOI: 10.1016/j.ijar.2025.109550

Eugenio Clerico

引用次数: 0

FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis FCPCA：基于共主成分分析的高维时间序列模糊聚类

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-29 DOI: 10.1016/j.ijar.2025.109552

Ziling Ma, Ángel López-Oriona, Hernando Ombao, Ying Sun

{"title":"FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis","authors":"Ziling Ma, Ángel López-Oriona, Hernando Ombao, Ying Sun","doi":"10.1016/j.ijar.2025.109552","DOIUrl":"10.1016/j.ijar.2025.109552","url":null,"abstract":"<div><div>Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional approaches, such as crisp clustering, rely on the assumption that clusters are sufficiently separated with little overlap. However, real-world data often defy this assumption, showing overlapping distributions or overlapping clouds of points and blurred boundaries between clusters. Fuzzy clustering offers a compelling alternative by allowing partial membership in multiple clusters, making it well-suited for these ambiguous scenarios. Despite its advantages, current fuzzy clustering methods primarily focus on univariate time series, and for multivariate cases, even datasets of moderate dimensionality become computationally prohibitive. This challenge is further exacerbated when dealing with time series of varying lengths, leaving a clear gap in addressing the complexities of modern datasets. This work introduces a novel fuzzy clustering approach based on common principal component analysis to address the aforementioned shortcomings. Our method has the advantage of efficiently handling high-dimensional multivariate time series by reducing dimensionality while preserving critical temporal features. Extensive numerical results show that our proposed clustering method outperforms several existing approaches in the literature. An interesting application involving brain signals from different drivers recorded from a simulated driving experiment illustrates the potential of the approach.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109552"},"PeriodicalIF":3.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925197","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 granular fusion: Graph-embedded rectangular neighborhood rough sets for knowledge system convergence 可解释的颗粒融合：用于知识系统收敛的图嵌入矩形邻域粗糙集

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-28 DOI: 10.1016/j.ijar.2025.109561

Yigao Li, Weihua Xu

{"title":"Explainable granular fusion: Graph-embedded rectangular neighborhood rough sets for knowledge system convergence","authors":"Yigao Li, Weihua Xu","doi":"10.1016/j.ijar.2025.109561","DOIUrl":"10.1016/j.ijar.2025.109561","url":null,"abstract":"<div><div>With the development of Rough Set Theory (RST), many improved theories based on RST have emerged. Some of these theories have been applied in the field of feature selection, significantly improving its efficiency. However, they have not yet been widely used in multi-source information domains. This paper proposes a multi-source information fusion method based on Granular-Rectangular Neighborhood Rough Set (GRNRS) and graph theory. First, an improved algorithm based on GRNRS is proposed to evaluate the contribution of each information source to a classification task under a specific attribute. In this process, we provided rigorous theoretical proofs for the concepts and mechanisms used in the improved GRNRS. Meanwhile, the Pearson Correlation Coefficient (PCC) is used to assess the linear relationship between information sources. Then, by integrating the results of the improved GRNRS algorithm and PCC, the adjacency matrix of a graph is constructed. Finally, the preference value of each information source under a specific attribute is calculated based on the adjacency matrix. Information fusion under a specific attribute is achieved by selecting the information source with the highest preference value. Extensive experiments are conducted to analyze the impact of the algorithm's parameters on its final performance. Meanwhile, our method is compared with seven other information fusion algorithms using three metrics: classification accuracy, Average Quality (AQ), and runtime. Friedman and Nemenyi tests are conducted on the comparison results under the classification accuracy and AQ metrics, demonstrating that there are significant differences among the algorithms. The results demonstrate that the proposed algorithm is both time-efficient and effective.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109561"},"PeriodicalIF":3.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922645","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

Self-supervised multi-level generative adversarial network data imputation algorithm 自监督多级生成对抗网络数据输入算法

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-27 DOI: 10.1016/j.ijar.2025.109553

Yi Xu , Shujuan Fang , Xuhui Xing

{"title":"Self-supervised multi-level generative adversarial network data imputation algorithm","authors":"Yi Xu , Shujuan Fang , Xuhui Xing","doi":"10.1016/j.ijar.2025.109553","DOIUrl":"10.1016/j.ijar.2025.109553","url":null,"abstract":"<div><div>Data missing has always been a challenging problem in machine learning. The Generative Adversarial Imputation Networks (GAIN) have been shown to outperform many existing solutions. However, in GAIN, because missing values lack ground truth as supervision, it is unable to construct reconstruction loss for missing values and can only judge the reasonableness of imputed values based on reconstruction loss of non-missing values and adversarial loss. From the perspective of granular computing, data has levels, and data at different levels of granularity encapsulates different knowledge. Therefore, based on granular computing, this paper proposes a self-supervised multi-level generative adversarial network data imputation algorithm (MGAIN). Firstly, multiple levels of data are constructed using nested feature set sequences. Then, GAIN is used to impute missing values at the coarsest granularity level, and the imputation results of missing values at the coarse granularity level are used as supervision for imputing missing values at the fine granularity level, constructing reconstruction loss for missing values at the fine granularity level. Finally, based on reconstruction loss of missing values, reconstruction loss of non-missing values, and adversarial loss, data at the finer granularity level is imputed. MGAIN imputes missing values level by level from the coarse granularity level to the fine granularity level to obtain more accurate imputation results. Experimental results validate the effectiveness of the proposed method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109553"},"PeriodicalIF":3.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925202","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