International Journal of Approximate Reasoning最新文献

英文中文

Multi-granularity Knowledge Fusion for Feature Selection Using Granular-ball Entropy Uncertainty Measures 基于颗粒球熵不确定性测度的特征选择多粒度知识融合

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-11-11 DOI: 10.1016/j.ijar.2025.109590

Kehua Yuan , Yuji Bai , Duoqian Miao , Weiping Ding , Yiyu Yao , Hongyun Zhang , Witold Pedrycz

Multi-granularity computing for knowledge discovery has emerged as a remarkable paradigm in data mining and machine learning. As a representative method, granular-ball computing has attracted considerable attention due to its efficiency and adaptability in handling complex data distributions. However, most existing granularity-based approaches focus on intra-granular mutual information while neglecting the heterogeneity and overlapping phenomena across granularities. This limitation often leads to imprecise knowledge space construction and inaccurate uncertainty estimation in feature evaluation. To overcome this problem, this study proposes a novel and high-efficiency multi-granularity knowledge fusion framework for feature selection, incorporating an enhanced granular-ball generation mechanism and a newly designed granular-ball entropy (GB-E) uncertainty measure. Specifically, we first develop an enhanced granular-ball generation mechanism to construct multi-granularity knowledge space by incorporating class distribution information, thus achieving more accurate and flexible data partitioning. Subsequently, by jointly analyzing the separation and aggregation among granular balls, a novel granular-ball entropy is proposed to quantify uncertainty in the multi-granularity knowledge space. Compared with existing uncertainty measure methods, it provides a dual-perspective uncertainty characterization and effectively improves the accuracy of granularity information fusion. Furthermore, two feature significance measures based on the proposed GB-E measure are introduced for feature evaluation, and then a corresponding feature selection method is developed. Extensive experiments on multiple public datasets demonstrate the proposed method’s superior classification performance compared with several state-of-the-art approaches.

面向知识发现的多粒度计算已经成为数据挖掘和机器学习领域的一个重要范例。作为一种代表性的方法，颗粒球计算因其处理复杂数据分布的效率和适应性而受到广泛关注。然而，现有的基于粒度的方法大多侧重于粒度内的互信息，而忽略了粒度间的异质性和重叠现象。这种局限性往往导致特征评价中知识空间的构建不精确，不确定性估计不准确。为了克服这一问题，本研究提出了一种新的、高效的多粒度知识融合框架用于特征选择，该框架结合了增强的颗粒球生成机制和新设计的颗粒球熵（GB-E）不确定性测度。具体而言，我们首先开发了一种增强的颗粒球生成机制，通过结合类分布信息构建多粒度知识空间，从而实现更准确、更灵活的数据分区。随后，通过对颗粒球之间的分离和聚集进行分析，提出了一种新的颗粒球熵来量化多粒度知识空间中的不确定性。与现有的不确定性度量方法相比，该方法提供了双视角的不确定性表征，有效提高了粒度信息融合的精度。在此基础上，引入了基于GB-E测度的两种特征显著性测度进行特征评价，并提出了相应的特征选择方法。在多个公共数据集上进行的大量实验表明，与几种最先进的方法相比，该方法具有优越的分类性能。

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引用次数: 0

Matrix-based efficient methods to update three-way regions in neighborhood systems under varying attributes 基于矩阵的邻域系统三向区域更新方法

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-11-10 DOI: 10.1016/j.ijar.2025.109598

Yingqi Qi , Chengxiang Hu , Xiaoling Huang

Traditional set-based methods for computing three-way regions in neighborhood systems primarily rely on the inclusion relationships between target concepts and neighborhood classes to process continuous numerical data. However, these methods exhibit significant limitations when applied to time-varying neighborhood information systems, as they inherently lack the capability to accommodate dynamically evolving data, effectively. To overcome this challenge, our research presents novel matrix-based incremental methods that leverage previously computed results to enable more efficient updating and maintenance of three-way regions in neighborhood rough sets. Through comprehensive integration and analysis of neighborhood information systems with a focus on varying attributes, we develop matrix-based incremental mechanisms. Building on these mechanisms, we propose two incremental algorithms to effectively handle dynamic numerical data. Experimental results demonstrate the effectiveness and superior efficiency of the proposed methods compared to existing approaches. Specifically, the proposed algorithms exhibit lower computational time and higher speed-up ratio, highlighting their efficiency for updating neighborhood three-way regions.

传统的基于集的邻域系统三向区域计算方法主要依靠目标概念和邻域类之间的包含关系来处理连续数值数据。然而，当应用于时变邻域信息系统时，这些方法表现出明显的局限性，因为它们固有地缺乏有效适应动态发展数据的能力。为了克服这一挑战，我们的研究提出了新的基于矩阵的增量方法，利用先前计算的结果来更有效地更新和维护邻域粗糙集中的三向区域。通过对不同属性邻域信息系统的综合集成和分析，提出了基于矩阵的增量机制。在这些机制的基础上，我们提出了两种增量算法来有效地处理动态数值数据。实验结果表明，与现有方法相比，所提方法具有较高的有效性和效率。具体而言，该算法具有较低的计算时间和较高的加速比，突出了其更新邻域三向区域的效率。

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引用次数: 0

Measuring external conflict in Dempster-Shafer theory based on Kantorovich problems 基于Kantorovich问题的Dempster-Shafer理论中的外部冲突度量

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-11-09 DOI: 10.1016/j.ijar.2025.109597

Andrey G. Bronevich , Alexander E. Lepskiy

In the paper, we consider three possible types of external conflict in Dempster-Shafer theory and propose its measurement based on functionals evaluating intersection, inclusion and distance between random sets. All proposed functionals can be viewed as extensions of known functionals like Jaccard metric, Jaccard index, and Dice coefficient from usual sets to random sets based on the solutions of the Kantorovich problems.

在本文中，我们考虑了Dempster-Shafer理论中三种可能的外部冲突类型，并提出了基于评估随机集之间的交集、包含和距离的泛函度量方法。所有提出的泛函都可以看作是已知泛函的扩展，如Jaccard度量、Jaccard指数和Dice系数，从通常集合到基于Kantorovich问题解的随机集合。

引用次数: 0

ARIPOTER: Solvers for approximate reasoning based on grounded semantics ARIPOTER：基于接地语义的近似推理求解器

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-11-09 DOI: 10.1016/j.ijar.2025.109599

Jérôme Delobelle , Jean-Guy Mailly , Julien Rossit

Efficient computation of hard reasoning tasks is a key issue in abstract argumentation. One recent approach is to define approximate algorithms, i.e. methods that provide an answer that may not always be correct, but outperform the exact algorithms regarding the computation runtime. One such approach proposes to use the grounded semantics, which is polynomially computable, as a starting point for determining whether arguments are (credulously or skeptically) accepted with respect to various other extension-based semantics. In this paper, we push further this idea by defining a general family of approaches to evaluate the acceptability of arguments which are not in the grounded extension, neither attacked by it. These approaches rely on gradual semantics to evaluate these arguments. We also propose an approach using an heuristic based on the number of arguments attacked by or attacking an argument, and we show that this last approach, although seemingly different, is actually also an instance of our general family of approaches based on gradual semantics. We have implemented our approaches and provided an empirical study in which we discuss the results and compare our approach with the state-of-the-art approximate algorithms.

硬推理任务的高效计算是抽象论证中的一个关键问题。最近的一种方法是定义近似算法，即提供的答案可能并不总是正确的，但在计算运行时方面优于精确算法的方法。其中一种方法建议使用可多项式计算的基础语义作为起点，以确定相对于其他各种基于扩展的语义，参数是否（可信地或怀疑地）被接受。在本文中，我们通过定义一组一般的方法来进一步推动这一思想，以评估不属于基础扩展的论点的可接受性，也不受其攻击。这些方法依赖于渐进语义来评估这些参数。我们还提出了一种方法，使用启发式方法，基于被攻击或攻击一个论点的论点的数量，我们表明，最后一种方法，尽管看起来不同，实际上也是我们基于渐进语义的一般方法家族的一个实例。我们已经实现了我们的方法，并提供了一项实证研究，我们讨论了结果，并将我们的方法与最先进的近似算法进行了比较。

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引用次数: 0

On exact regions between measures of concordance and Chatterjee’s rank correlation for lower semilinear copulas 关于下半线性联结的一致性测度与查特吉等级相关之间的精确区域

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-11-02 DOI: 10.1016/j.ijar.2025.109588

Sebastian Fuchs, Carsten Limbach, Fabian Schürrer

We explore how the classical concordance measures–Kendall’s

τ

, Spearman’s rank correlation

ρ

, and Spearman’s footrule

ϕ

–relate to Chatterjee’s rank correlation

ξ

when restricted to lower semilinear copulas. First, we provide a complete characterization of the attainable

τ - ρ

region for this class, thus resolving the conjecture of Maislinger and Trutschnig [1]. Building on this result, we then derive the exact

τ - ϕ

and

ϕ - ρ

regions, obtain a closed-form relationship between

ξ

and

τ

, and establish the exact

τ - ξ

region. In particular, we prove that

ξ

never exceeds

τ

ρ

, or

ϕ

. Our results clarify the relationship between undirected and directed dependence measures and reveal novel insights into the dependence structures that result from lower semilinear copulas.

我们探讨了经典的一致性测量- kendall的τ， Spearman的秩相关ρ和Spearman的阶导数 -在限制于较低的半线性联结时如何与Chatterjee的秩相关ξ相关。首先，我们给出了该类可得τ−ρ区域的完整表征，从而解决了Maislinger和Trutschnig的猜想。在此结果的基础上，我们推导出精确的τ−φ和φ−ρ区域，得到ξ和τ之间的封闭形式关系，并建立精确的τ−ξ区域。特别地，我们证明了ξ从不超过τ， ρ，或φ。我们的研究结果阐明了无向和有向依赖度量之间的关系，并揭示了对由低半线性联结引起的依赖结构的新见解。

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引用次数: 0

New paraconsistent modal logics based on rough modus ponens rules and their interrelations 基于粗糙模态规则及其相互关系的新准一致模态逻辑

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-11-01 DOI: 10.1016/j.ijar.2025.109587

Bidhan Saha , Mohua Banerjee , Soma Dutta

Several logics were proposed by Bunder, Chakraborty and Banerjee using different kinds of ‘rough modus ponens’ rules [1, 2, 3]. Some of these logics are paraconsistent, but most are explosive. We observe that weakening the rules in a particular manner generates a collection of new paraconsistent modal logics. Several properties of the new logics are studied. One such is the characterization of the syntactic consequence relations, which yields a collection of non-standard notions of semantic consequence. Interrelations between the proposed logics and the logics introduced in [3] are also investigated.

Bunder， Chakraborty和Banerjee使用不同类型的“粗糙模态”规则提出了几种逻辑[1,2,3]。其中一些逻辑是不一致的，但大多数是爆炸性的。我们观察到，以一种特定的方式削弱规则会产生一组新的副一致模态逻辑。研究了新逻辑的几个性质。其中之一是句法结果关系的表征，它产生了一系列非标准的语义结果概念。提出的逻辑与[3]中引入的逻辑之间的相互关系也进行了研究。

引用次数: 0

A robust multi-source transfer classification method based on belief functions for cross-domain pattern recognition 基于信念函数的跨域模式识别鲁棒多源转移分类方法

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-10-30 DOI: 10.1016/j.ijar.2025.109589

Linqing Huang , Jinfu Fan , Gongshen Liu , Shilin Wang

In pattern recognition with few or no labeled data, domain adaptation techniques are often used to transfer knowledge in the source domain to help build classification models in the target domain. The effective combination of complementary information in multiple source domains usually can further improve the classification accuracy. To this end, we present a Robust Multi-Source Transfer (RMST) classification method consisting of two-step fusion of classifiers to extract the useful information in each source domain as much as possible, and to effectively combine the complementary information using belief functions in different source domains. The first step is to accurately predict the pseudo labels when computing conditional distribution, in order to learn a robust new feature representation, which is used to fuse diverse classifiers learnt by patterns for reliable soft classification in the second step of our algorithm. Furthermore, the soft classification results yielded by the assistance of different source domains are combined by belief functions with the new weighting factors taking into account the distribution discrepancy and classifier’s performance. The effectiveness of RMST was evaluated with respect to a variety of advanced methods, and the experimental results show that RMST can significantly improve the classification accuracy.

在很少或没有标记数据的模式识别中，通常使用领域自适应技术将源领域的知识转移到目标领域，以帮助构建目标领域的分类模型。多源域互补信息的有效组合通常可以进一步提高分类精度。为此，提出了一种两步融合分类器的鲁棒多源转移（RMST）分类方法，以尽可能多地提取每个源域的有用信息，并利用不同源域的信念函数有效地组合互补信息。第一步是在计算条件分布时准确预测伪标签，以学习一种鲁棒的新特征表示，用于融合由模式学习到的各种分类器进行可靠的软分类。此外，考虑分布差异和分类器性能，将不同源域辅助下的软分类结果与加权因子相结合。对比多种先进的分类方法对RMST的有效性进行了评价，实验结果表明RMST可以显著提高分类精度。

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引用次数: 0

Modeling the selection of representative aggregation functions for optimizing the representation of group behavior preferences within the preference disaggregation framework 在偏好分解框架下，为优化群体行为偏好的表示，对代表性聚合函数的选择进行建模

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-10-26 DOI: 10.1016/j.ijar.2025.109586

Zaiwu Gong , Jiaxin Zhou , Xinxin Luo , Kun Zhou , Weiwei Guo , Guo Wei

In Multiple Criteria Decision Aid (MCDA), preference disaggregation is used to derive aggregation functions that replicate the preference information provided by decision makers (DMs). A key research challenge is identifying the most representative aggregation function that aligns with the DMs’ behavioral preferences from all feasible functions. In the context of group decisions and negotiations, preference disaggregation faces two main barriers: Selecting representative aggregation functions and reducing the cognitive effort required from DMs. In this paper, we extend preference disaggregation to group decision-making by enabling DMs to increase the diversity of their decision matrices, and establish several representative aggregation function selection models that account for group behavioral preferences. Given the cognitive limitations of DMs, their preferences are often expressed as confidence levels, which inherently involve uncertainty. Therefore, we expand the scope of preference information required for preference disaggregation from deterministic preferences to uncertain preferences. By applying probability theory–specifically, chance constraints–we transform DMs’ uncertain preferences into constraints for selecting representative aggregation functions that align with their behavioral preferences. Notably, when the confidence level approaches 100 %, the uncertain preferences converge to their deterministic counterparts, demonstrating the extensiveness and inclusiveness of uncertain preference information. Finally, we illustrate the practical feasibility of our approach through a case study on the evaluation of medical pension institutions.

在多准则决策辅助（MCDA）中，偏好分解用于派生聚合函数，该函数复制决策者（dm）提供的偏好信息。一个关键的研究挑战是从所有可行的函数中找出与dm的行为偏好相一致的最具代表性的聚合函数。在群体决策和协商的背景下，偏好分解面临两个主要障碍：选择具有代表性的聚合功能和减少dm需要的认知努力。本文通过增加决策矩阵的多样性，将偏好分解扩展到群体决策中，并建立了几个代表群体行为偏好的聚集函数选择模型。考虑到dm的认知局限性，他们的偏好通常表现为信心水平，这本身就包含不确定性。因此，我们将偏好分解所需的偏好信息范围从确定性偏好扩展到不确定性偏好。通过应用概率论，特别是机会约束，我们将dm的不确定偏好转化为选择符合其行为偏好的代表性聚合函数的约束。值得注意的是，当置信水平接近100%时，不确定偏好收敛于它们的确定性对应物，显示了不确定偏好信息的广泛性和包容性。最后，以医疗养老机构评价为例，说明本文方法的现实可行性。

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Granular structures of covering-based rough set approximations through the Mapper algorithm 通过Mapper算法实现基于覆盖的粗糙集近似的颗粒结构

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-10-14 DOI: 10.1016/j.ijar.2025.109585

Wannes De Maeyer , Mauricio Restrepo , Chris Cornelis

Covering-based rough sets (CBRS) offer a flexible extension of classical rough set theory, making them well-suited for analyzing diverse types of complex and possibly inconsistent data. A requirement for the effective use of CBRS is the availability of high-quality coverings of the dataset. A promising approach for generating such coverings is the Mapper algorithm, which builds on the underlying topological structure of the data. However, Mapper's performance depends heavily on the choice of its parameters. In this paper, we study how the structure of the parameter space is transferred to the structure of the coverings generated by these parameters, focusing on the weak and strong approximation operators. We prove important monotonicity results that give insights into the inner workings of the Mapper algorithm and how it can generate coverings. We give an overview of how each parameter influences the output covering and illustrate this using real-world datasets.

基于覆盖的粗糙集（CBRS）提供了经典粗糙集理论的灵活扩展，使其非常适合于分析各种类型的复杂和可能不一致的数据。有效使用CBRS的一个要求是数据集的高质量覆盖的可用性。生成这种覆盖的一种很有前途的方法是Mapper算法，它建立在数据的底层拓扑结构之上。然而，Mapper的性能在很大程度上取决于其参数的选择。在本文中，我们研究了如何将参数空间的结构转化为由这些参数生成的覆盖的结构，重点研究了弱和强逼近算子。我们证明了重要的单调性结果，这些结果可以深入了解Mapper算法的内部工作原理以及它如何生成覆盖。我们概述了每个参数如何影响输出覆盖，并使用实际数据集来说明这一点。

引用次数: 0

Special issue on the 11th International Conference on Soft Methods in Probability and Statistics (SMPS 2024) 第十一届国际概率与统计软方法会议（SMPS 2024）特刊

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-10-09 DOI: 10.1016/j.ijar.2025.109584

Sebastian Fuchs

引用次数: 0

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