International Journal of Approximate Reasoning最新文献_第7页

Distribution assessment-based multiple over-sampling with evidence fusion for imbalanced data classification 基于分布评估的多重过采样与证据融合的不平衡数据分类

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

International Journal of Approximate Reasoning

Pub Date : 2025-08-06 DOI: 10.1016/j.ijar.2025.109538

Hongpeng Tian , Zuowei Zhang , Zhunga Liu , Jingwei Zuo , Caixing Yang

Over-sampling methods concentrate on creating balanced samples and have proven successful in classifying imbalanced data. However, current over-sampling methods fail to consider the uncertainty of produced samples, potentially altering the data distribution and impacting the classification process. To address this issue, we propose a distribution assessment-based multiple over-sampling (DAMO) method for classifying imbalanced data. We first introduce a multiple over-sampling method based on distribution assessment to create different forms of synthetic samples. The core is quantifying the inconsistency of data distribution before and after sampling as a constraint to guide multiple over-sampling, thereby minimizing the data shift and characterizing the uncertainty of produced samples. Then, we quantify the local reliability of the classification results and select several imprecise samples with low local reliability that are indistinguishable between classes. Neighbors serve as additional complementary information to calibrate the results of imprecise samples, thereby reducing the likelihood of misclassification. The calibrated results are combined by the discounting Dempster-Shafer fusion rule to make a final decision. DAMO's efficiency has been demonstrated through comparisons with related methods on various real imbalanced datasets.

过度抽样方法专注于创建平衡样本，并已被证明在分类不平衡数据方面是成功的。然而，目前的过度抽样方法没有考虑到产生样本的不确定性，这可能会改变数据分布并影响分类过程。为了解决这个问题，我们提出了一种基于分布评估的多重过采样（DAMO）方法来对不平衡数据进行分类。我们首先介绍了基于分布评估的多重过采样方法来创建不同形式的合成样本。其核心是量化采样前后数据分布的不一致性，作为约束来指导多次过采样，从而最大限度地减少数据的移位，表征所产生样本的不确定性。然后，对分类结果的局部信度进行量化，选取局部信度较低且类间无法区分的不精确样本。邻域作为额外的补充信息来校准不精确样本的结果，从而减少误分类的可能性。将标定结果结合贴现Dempster-Shafer融合规则进行最终决策。通过与相关方法在各种实际不平衡数据集上的比较，证明了DAMO的有效性。

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引用次数: 0

A novel three-way based self-adaptive filtering model for sentiment analysis 一种新的基于三向自适应的情感分析模型

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

International Journal of Approximate Reasoning

Pub Date : 2025-08-05 DOI: 10.1016/j.ijar.2025.109536

Zhihui Zhang, Dun Liu, Rongping Shen

In the era of social media and diverse communication platforms, understanding human emotion across various modalities has become a crucial challenge. While significant progress has been made in feature extraction and interaction techniques, several unresolved issues persist, particularly concerning the balance between these two aspects. A central question is whether all extracted features are of equal importance, or if some may contain redundant or noisy information that undermines effective modality interaction. To address these challenges, we propose a novel Three-Way Decision-Based Self-Adaptive Filtering Model (TWSAFM). Inspired by the three-way decision (TWD) theory, we introduce a self-adaptive filtering module that categorizes extracted modal features into three distinct domains: acceptable, rejectable, and reconsidering. This classification allows for separate processing of features, enabling the model to prioritize essential information while minimizing the impact of redundant and noisy data. Experimental validation on three benchmark datasets demonstrates that TWSAFM outperforms state-of-the-art methods in sentiment analysis tasks. Furthermore, training studies and parameter sensitivity analysis underscore the effectiveness of TWSAFM in efficiently filtering out irrelevant and noisy features, highlighting its robust contribution to enhancing feature interaction.

在社交媒体和多种交流平台的时代，跨多种方式理解人类情感已成为一项至关重要的挑战。虽然在特征提取和交互技术方面取得了重大进展，但仍然存在一些未解决的问题，特别是关于这两个方面之间的平衡。一个核心问题是，是否所有提取的特征都同等重要，或者是否有些特征可能包含冗余或噪声信息，从而破坏有效的模态交互。为了解决这些挑战，我们提出了一种新的基于决策的三向自适应滤波模型（TWSAFM）。受三向决策（TWD）理论的启发，我们引入了一个自适应滤波模块，该模块将提取的模态特征分为三个不同的领域：可接受的、可拒绝的和重新考虑的。这种分类允许对特征进行单独处理，使模型能够优先考虑基本信息，同时最大限度地减少冗余和噪声数据的影响。在三个基准数据集上的实验验证表明，TWSAFM在情感分析任务中优于最先进的方法。此外，训练研究和参数灵敏度分析强调了TWSAFM在有效滤除不相关和噪声特征方面的有效性，突出了其对增强特征交互的鲁棒性贡献。

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引用次数: 0

Domain-informed and neural-optimized belief assignments: A framework applied to cultural heritage 领域信息和神经优化的信念分配：一个应用于文化遗产的框架

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

International Journal of Approximate Reasoning

Pub Date : 2025-08-05 DOI: 10.1016/j.ijar.2025.109534

Sofiane Daimellah , Sylvie Le Hégarat-Mascle , Clotilde Boust

Identifying pigments in Cultural Heritage artifacts is key to uncovering their origin and guiding conservation strategies. Although recent advances in non-invasive imaging have enabled the collection of rich multimodal data, existing methods often fall short in dealing with uncertain, ambiguous, or noisy information. This paper introduces a versatile fusion framework grounded in Belief Function Theory, combining domain-informed evidence modeling with neural optimization. Specifically, we propose a general strategy for assigning mass functions by leveraging expert knowledge encoded in parametric Evidence Mapping Functions, which are further refined through task-specific training using constrained neural networks. When applied to pigment classification, our method demonstrates robustness against source variability and class ambiguity. Experiments conducted on both synthetic and mock-up datasets validate its effectiveness and suggest promising potential for broader applications.

识别文化遗产文物中的颜料是揭示其来源和指导保护策略的关键。尽管最近在非侵入性成像方面的进展使收集丰富的多模态数据成为可能，但现有的方法在处理不确定、模糊或有噪声的信息时往往存在不足。本文介绍了一种基于信念函数理论的多功能融合框架，将领域知情证据建模与神经网络优化相结合。具体来说，我们提出了一种通过利用编码在参数化证据映射函数中的专家知识来分配质量函数的一般策略，并通过使用约束神经网络进行任务特定训练来进一步改进。当应用于颜料分类时，我们的方法对源可变性和类歧义具有鲁棒性。在合成数据集和模型数据集上进行的实验验证了其有效性，并表明其具有更广泛应用的潜力。

引用次数: 0

Sensitivity analysis to unobserved confounding with copula-based normalizing flows 基于copula的归一化流对未观测混杂的敏感性分析

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-30 DOI: 10.1016/j.ijar.2025.109531

Sourabh Balgi , Marc Braun , Jose M. Peña , Adel Daoud

We propose a novel method for sensitivity analysis to unobserved confounding in causal inference. The method builds on a copula-based causal graphical normalizing flow that we term ρ-GNF, where

ρ \in [- 1, + 1]

is the sensitivity parameter. The parameter represents the non-causal association between exposure and outcome due to unobserved confounding, which is modeled as a Gaussian copula. In other words, the ρ-GNF enables scholars to estimate the average causal effect (ACE) as a function of ρ, accounting for various confounding strengths. The output of the ρ-GNF is what we term the

ρ_{c u r v e}

, which provides the bounds for the ACE given an interval of assumed ρ values. The

ρ_{c u r v e}

also enables scholars to identify the confounding strength required to nullify the ACE. We also propose a Bayesian version of our sensitivity analysis method. Assuming a prior over the sensitivity parameter ρ enables us to derive the posterior distribution over the ACE, which enables us to derive credible intervals. Finally, leveraging on experiments from simulated and real-world data, we show the benefits of our sensitivity analysis method.

我们提出了一种对因果推理中未观察到的混杂因素进行敏感性分析的新方法。该方法建立在一个基于copula的因果图归一化流上，我们称之为ρ- gnf，其中ρ∈[−1，+1]是灵敏度参数。该参数表示由于未观察到的混杂而导致的暴露与结果之间的非因果关联，其建模为高斯联结。换句话说，ρ- gnf使学者能够估计平均因果效应（ACE）作为ρ的函数，考虑到各种混杂强度。ρ- gnf的输出就是我们所说的ρ曲线，它提供了给定假定ρ值区间的ACE的界。ρ曲线还使学者能够识别使ACE无效所需的混杂强度。我们还提出了灵敏度分析方法的贝叶斯版本。假设灵敏度参数ρ的先验使我们能够推导出ACE的后验分布，从而使我们能够推导出可信区间。最后，利用模拟和真实数据的实验，我们展示了灵敏度分析方法的优点。

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引用次数: 0

Triadic data: Representation and reduction 三元数据：表示与约简

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-29 DOI: 10.1016/j.ijar.2025.109532

Léa Aubin Kouankam Djouohou , Blaise Blériot Koguep Njionou , Leonard Kwuida

Triadic Concept Analysis (TCA) is an extension of Formal Concept Analysis (FCA) for handling data represented as a set of objects described by attributes and conditions via a ternary relation. However, the intuition to go from FCA to TCA is not always straightforward. In this paper we discuss some FCA notions from dyadic to triadic. Although some ideas admit straightforward adaptation, most do not. In particular, we address the representation problem, the notion of redundant attributes and subcontexts in the triadic setting.

三元概念分析（TCA）是形式概念分析（FCA）的扩展，用于处理通过三元关系由属性和条件描述的一组对象表示的数据。然而，从FCA到TCA的直觉并不总是直截了当的。本文讨论了从二进到三进的FCA概念。尽管有些想法允许直接适应，但大多数想法不允许。特别是，我们解决了表示问题，冗余属性和子上下文的概念在三元设置。

引用次数: 0

Optimizing connectivity in fuzzy graphs for resilient disaster response networks 弹性灾害响应网络模糊图连通性优化

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-29 DOI: 10.1016/j.ijar.2025.109535

P Sujithra , Sunil Mathew , J.N. Mordeson

Despite significant technological advances in recent years, communication challenges still persist. These issues are especially evident during crises, where system failures, network overloads, and incompatibilities among the communication technologies used by different organizations create major obstacles. Catastrophe scenarios are marked by high information uncertainty and limited control, which raises challenges for crisis communication. However, these aspects remain underexplored from a network-theoretic perspective. This study investigates the

(x, y)

-connectivity parameter between two nodes in a fuzzy graph, offering insights into network structure, robustness, and performance. We introduce a novel classification of nodes and edges into three categories: enhancing, eroded, and persisting, based on their impact on node-to-node connectivity. The behavior of these classifications is analyzed across different classes of fuzzy graphs. Furthermore, we establish upper and lower bounds for the

(x, y)

-connectivity under two graph operations. An efficient algorithm is proposed to identify and categorize nodes and edges accordingly. The practical relevance of our classification is illustrated through its application to disaster response communication networks, where maintaining resilient and adaptive communication is critical.

尽管近年来取得了重大的技术进步，但通信挑战仍然存在。这些问题在危机期间尤其明显，在危机期间，系统故障、网络过载以及不同组织使用的通信技术之间的不兼容造成了主要障碍。灾难情景具有信息不确定性高、控制有限的特点，给危机沟通带来了挑战。然而，从网络理论的角度来看，这些方面还没有得到充分的探讨。本研究调查了模糊图中两个节点之间的（x,y）连接参数，提供了对网络结构，鲁棒性和性能的见解。基于对节点到节点连通性的影响，我们将节点和边分为三类：增强、侵蚀和持久。在不同类别的模糊图中分析了这些分类的行为。进一步，我们建立了两种图运算下(x,y)-连通性的上界和下界。提出了一种有效的节点和边的识别和分类算法。我们的分类的实际意义是通过它在灾难响应通信网络中的应用来说明的，其中保持弹性和适应性通信是至关重要的。

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引用次数: 0

Generalized conjunction and disjunction of two conditional events in the setting of conditional random quantities 条件随机量下两个条件事件的广义合取与析取

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-28 DOI: 10.1016/j.ijar.2025.109533

Lydia Castronovo , Giuseppe Sanfilippo

In recent papers, notions of conjunction and disjunction of two conditional events as suitable conditional random quantities, which satisfy basic probabilistic properties, have been deepened in the setting of coherence. In this framework, the conjunction and the disjunction of two conditional events are defined as five-valued objects, among which are the values of the (subjectively) assigned probabilities of the two conditional events. In the present paper we propose a generalization of these structures, where these new objects, instead of depending on the probabilities of the two conditional events, depend on two arbitrary values

a, b

in the unit interval. We show that they are connected by a generalized version of the De Morgan's law and, by means of a geometrical approach, we compute the lower and upper bounds on these new objects both in the precise and the imprecise case. Moreover, some particular cases, obtained for specific values of a and b or in case of some logical relations, are analyzed. The results of this paper lead to the conclusion that the only objects satisfying all the logical and the probabilistic properties already valid for the operations between events are the ones depending on the probabilities of the two conditional events.

近年来，在相干性的背景下，深化了两个条件事件作为满足基本概率性质的条件随机量的合取和析取的概念。在该框架中，将两个条件事件的合取和析取定义为五值对象，其中五值对象为两个条件事件（主观）赋值概率的值。在本文中，我们提出了这些结构的推广，其中这些新对象不是依赖于两个条件事件的概率，而是依赖于单位区间内的两个任意值a，b。我们用广义的德摩根定律证明了它们之间的联系，并通过几何方法计算了这些新对象在精确和不精确情况下的下界和上界。此外，还分析了a和b的特定值或某些逻辑关系下的一些特殊情况。本文的结果表明，满足事件间运算所有有效的逻辑和概率性质的对象是依赖于两个条件事件的概率的对象。

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引用次数: 0

Explainable multi-criteria decision-making: A three-way decision perspective 可解释的多准则决策：三向决策视角

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-07-18 DOI: 10.1016/j.ijar.2025.109528

Chengjun Shi, Yiyu Yao

This paper proposes an Explainable Multi-Criteria Decision-Making (XMCDM) framework that constructs trilevel explanations with respect to classic multi-criteria decision-making methods. The framework consists of explainable data preparation, explainable decision analysis, and explainable decision support, which integrates ideas from three-way decision and symbols-meaning-value spaces. First, we briefly introduce the key concepts at each level and list potential issues to be resolved, including gathering multi-criteria data, interpreting multi-criteria decision-making working principles, and offering effective outcome presentation. We examine existing literature that solves part of those questions and point out that rule-based explanations may be applicable and efficient to explain ranking/ordering results. Then, we discuss two methods that generate three-way rankings with respect to an individual criterion and integrate three-way rankings with multi-criteria ranking. We modify the Iterative Dichotomiser 3 algorithm to build rule-based explanations. Finally, after giving a small illustrative example, we design experiments on five real-life datasets, test explainability of three classic multi-criteria decision-making methods, and tune the thresholds. The experimental results demonstrate that our proposed framework is feasible and adaptable to various data characteristics.

本文提出了一个可解释的多准则决策（XMCDM）框架，该框架针对经典的多准则决策方法构建了三级解释。该框架由可解释的数据准备、可解释的决策分析和可解释的决策支持组成，融合了三方决策和符号-意义-价值空间的思想。首先，我们简要介绍了每个级别的关键概念，并列出了需要解决的潜在问题，包括收集多标准数据，解释多标准决策工作原理，以及提供有效的结果展示。我们研究了解决这些问题的现有文献，并指出基于规则的解释可能适用且有效地解释排名/排序结果。然后，我们讨论了基于单个标准生成三向排名的两种方法，并将三向排名与多标准排名相结合。我们修改了迭代二分器3算法来构建基于规则的解释。最后，在给出一个小示例后，我们在五个实际数据集上设计了实验，测试了三种经典多准则决策方法的可解释性，并调整了阈值。实验结果表明，该框架是可行的，并能适应各种数据特征。

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引用次数: 0

Fusing fuzzy rough sets and mean shift for anomaly detection 基于模糊粗糙集和均值移位的异常检测

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-18 DOI: 10.1016/j.ijar.2025.109530

Mengyao Liao , Zhiyu Chen , Can Gao , Jie Zhou , Xiaodong Yue

Outlier detection is a critical but challenging task due to the complex distribution of practical data, and some Fuzzy Rough Sets (FRS)-based methods have been presented to identify outliers from these data. However, these methods still have limitations when facing the co-existence of different types of outliers. In this study, an improved FRS-based unsupervised anomaly detection method is proposed by integrating distance and density information. Specifically, to detect the local outliers, a fuzzy granule density is first defined by introducing a Gaussian kernel similarity to characterize the local density of samples. Then, optimistic and pessimistic fuzzy granule densities are further developed to evaluate the density variation in the local neighborhood. Moreover, a distance measure based on mean shift is introduced to detect global and group outliers. Finally, an outlier detection method that integrates the density and distance measures is designed to effectively identify diverse types of outliers. Extensive experiments on synthetic and public datasets, along with statistical significance analysis, demonstrate the superior performance of the proposed method, achieving an average improvement of at least 12.27% in terms of AUROC.

由于实际数据的复杂分布，异常值检测是一项关键但具有挑战性的任务，一些基于模糊粗糙集（FRS）的方法已经提出了从这些数据中识别异常值的方法。然而，这些方法在面对不同类型离群值共存时仍然存在局限性。本文提出了一种改进的基于frs的无监督异常检测方法，将距离和密度信息相结合。具体来说，为了检测局部异常值，首先通过引入高斯核相似度来定义模糊颗粒密度来表征样本的局部密度。然后，进一步发展乐观和悲观模糊颗粒密度来评价局部邻域的密度变化。此外，还引入了一种基于均值位移的距离度量来检测全局异常点和组异常点。最后，设计了一种融合密度和距离测度的离群点检测方法，有效识别不同类型的离群点。在合成和公共数据集上进行的大量实验以及统计显著性分析表明，所提出的方法具有优越的性能，在AUROC方面平均提高了至少12.27%。

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引用次数: 0

Maximal consistent blocks-based optimistic and pessimistic probabilistic rough fuzzy sets and their applications in three-way multiple attribute decision-making 基于最大一致块的乐观和悲观概率粗糙模糊集及其在三向多属性决策中的应用

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2025-07-17 DOI: 10.1016/j.ijar.2025.109529

Yan Sun , Bin Pang , Ju-Sheng Mi , Wei-Zhi Wu

The integration of three-way decision (3WD) into multiple attribute decision-making (MADM) problems has emerged as a pivotal research area. 3WD can effectively manage the inherent uncertainty within the decision-making process. Additionally, it offers a semantic interpretation of the outcomes. In this paper, we introduce two innovative 3WD-MADM approaches, with a focus on granule selection and the handling of multi-type information in the framework of three-way decisions. Firstly, we construct maximal consistent blocks (MCBs)-based pessimistic and optimistic probabilistic rough fuzzy set (RFS) models and investigate their properties to ascertain their efficacy and reliability in decision-making contexts. Then, we define relative loss functions associated with “good state” and “bad state” scenarios. Building on this, we introduce four types of 3WDs based on our newly proposed optimistic and pessimistic probabilistic RFSs. Furthermore, we integrate the 3WDs information from both scenarios to formulate optimistic and pessimistic 3WD-MADM approaches, handling both single-valued fuzzy and intuitionistic fuzzy information. Finally, we contrast our proposed methodologies with the current MADM methods, and demonstrate their validity, significance and generalization ability.

将三向决策（three-way decision, 3WD）整合到多属性决策（MADM）问题中已经成为一个关键的研究领域。3WD可以有效管理决策过程中固有的不确定性。此外，它还提供了结果的语义解释。在本文中，我们介绍了两种创新的3WD-MADM方法，重点关注颗粒选择和在三方决策框架下多类型信息的处理。首先，构建了基于最大一致块（mcb）的悲观和乐观概率粗糙模糊集（RFS）模型，并研究了它们的性质，以确定它们在决策环境中的有效性和可靠性。然后，我们定义了与“好状态”和“坏状态”场景相关的相对损失函数。在此基础上，我们介绍了基于我们新提出的乐观和悲观概率rfs的四种类型的3wd。在此基础上，我们将两种场景下的3wd信息进行整合，形成乐观和悲观的3WD-MADM方法，分别处理单值模糊信息和直觉模糊信息。最后，我们将所提出的方法与现有的MADM方法进行了对比，验证了其有效性、显著性和泛化能力。

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引用次数: 0