International Journal of Approximate Reasoning最新文献

英文中文

FDACNet: Enhancing time-series classification with fuzzy feature and integrated self-attention and temporal convolution FDACNet：利用模糊特征，结合自关注和时间卷积增强时间序列分类

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-02 DOI: 10.1016/j.ijar.2025.109521

Xiuwei Chen, Li Lai, Maokang Luo

Time-series classification is crucial in time series analysis and holds significant importance in real-world scenarios. Applying self-attention and temporal convolution techniques is paramount when dealing with time series data. The self-attention mechanism enables the capture of correlations between different time steps in a sequence, thereby facilitating the handling of long-term dependencies. Meanwhile, temporal convolution is designed explicitly for processing time series data, effectively capturing temporal dependencies through convolutional layers. The integration of the two technologies plays a pivotal role in time series analysis, enabling accurate temporal classification. This paper proposes a novel net with fuzzy features and integrated self-attention and temporal convolution, denoted as FDACNet. The proposed net introduces two key components: FD-FE for fuzzy dominated feature extraction, and ATCmix for integrating self-attention and temporal convolution. FD-FE captures trend information by defining gradient relationship between time points within a time series sample. On the other hand, ATCmix combines convolution and self-attention to reduce parameters and enhance efficiency in handling time-series data. Finally, the proposed method is evaluated on twenty datasets and compared against twelve other state-of-the-art approaches. Experimental results demonstrate the superior classification accuracy of the proposed model, showcasing a 5.2% and 7.1% enhancement in average accuracy compared to the state-of-the-art convolution-based and transformer-based methods ModernTCN and iTransformer.

时间序列分类在时间序列分析中至关重要，在现实场景中具有重要意义。在处理时间序列数据时，应用自关注和时间卷积技术是至关重要的。自注意机制支持捕获序列中不同时间步之间的相关性，从而促进对长期依赖性的处理。同时，时间卷积被明确设计用于处理时间序列数据，通过卷积层有效捕获时间依赖性。两种技术的融合在时间序列分析中起着关键作用，实现了准确的时间分类。本文提出了一种集自关注和时间卷积于一体的模糊特征网络，称为FDACNet。该网络引入了两个关键组件：FD-FE用于模糊主导特征提取，ATCmix用于集成自关注和时间卷积。FD-FE通过定义时间序列样本中时间点之间的梯度关系来捕获趋势信息。另一方面，ATCmix将卷积和自关注相结合，减少了参数，提高了处理时间序列数据的效率。最后，该方法在20个数据集上进行了评估，并与其他12种最先进的方法进行了比较。实验结果表明，所提出的模型具有较高的分类精度，与基于卷积和基于变压器的方法相比，平均准确率分别提高了5.2%和7.1%。

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引用次数: 0

PAC learning of concept inclusions for ontology-mediated query answering 本体中介查询应答中概念包含的PAC学习

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

International Journal of Approximate Reasoning

Pub Date : 2025-07-02 DOI: 10.1016/j.ijar.2025.109523

Sergei Obiedkov , Barış Sertkaya

We present a probably approximately correct algorithm for learning the terminological part of a description-logic knowledge base via subsumption queries. The axioms we learn are concept inclusions between conjunctions of concepts from a specified set of concept descriptions. By varying the distribution of queries posed to the oracle, we adapt the algorithm to improve the recall when using the resulting TBox for ontology-mediated query answering. Experimental evaluation on OWL 2 EL ontologies suggests that our approach helps significantly improve recall while maintaining a high precision of query answering.

我们提出了一种可能近似正确的算法，通过包含查询来学习描述逻辑知识库的术语部分。我们学习的公理是概念的概念包含在概念描述的特定集合中的概念的连词之间。通过改变向oracle提出的查询的分布，我们调整了算法，以提高在使用结果TBox进行本体中介查询应答时的召回率。对owl2 EL本体的实验评估表明，我们的方法有助于显著提高召回率，同时保持较高的查询回答精度。

引用次数: 0

Double Boolean algebras: Constructions, sub-structures and morphisms 二重布尔代数：构造、子结构和态射

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-27 DOI: 10.1016/j.ijar.2025.109519

Gael Tenkeu Kembang , Yannick Léa Tenkeu Jeufack , Etienne Romuald Temgoua Alomo , Leonard Kwuida

Double Boolean algebras are algebras

\underline{D} : = (D; ⊓, ⊔, \neg, ⌟, ⊥, ⊤)

of type

(2, 2, 1, 1, 0, 0)

introduced by Rudolf Wille to capture the equational theory of the algebra of protoconcepts. Every double Boolean algebra

\underline{D}

contains two Boolean algebras:

{\underline{D}}_{⊓}

and

{\underline{D}}_{⊔}

. Three main goals are achieved in this paper. First we characterize sub-algebras of a double Boolean algebra

\underline{D}

as join sets of sub-algebras of the Boolean algebras

{\underline{D}}_{⊓}

and

{\underline{D}}_{⊔}

and a subset of

D ﹨ D_{p}

(where

D_{p} = D_{⊓} \cup D_{⊔}

) satisfying certain conditions. Second, we characterize homomorphisms between two double Boolean algebras

\underline{D}

and

\underline{E}

by homomorphisms between the Boolean algebras

{\underline{D}}_{⊓}

and

{\underline{E}}_{⊓}

{\underline{D}}_{⊔}

and

{\underline{E}}_{⊔}

and maps between

D ﹨ D_{p}

and E satisfying certain conditions. Third, we give some tools to construct some classes of pure double Boolean algebras.

二重布尔代数是由Rudolf Wille引入的类型为（2,2,1,1,0,0）的代数D_:=(D; ,≠，¬，⌟，⊥,⊥)，用于捕获原概念代数的方程理论。每一个二重布尔代数D_都包含两个布尔代数：D_≠和D_≠。本文实现了三个主要目标。首先，我们将二重布尔代数D_的子代数表征为满足一定条件的布尔代数D_ (t)和D_ (t)的子代数与DDp（其中Dp=D (t)）的子集DDp的子代数的连接集。其次，通过布尔代数D_ *和E_ *、D_ *和E_ *之间的同态以及DDp和E之间满足一定条件的映射，刻画了两个二重布尔代数D_和E_之间的同态。第三，给出了构造纯二重布尔代数的一些工具。

引用次数: 0

Joining copulas of extreme implicit dependence copulas 极端隐式依赖联结的联结联结

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-23 DOI: 10.1016/j.ijar.2025.109518

Noppawit Yanpaisan, Tippawan Santiwipanont, Songkiat Sumetkijakan

Copulas of uniform-

(0, 1)

random variables U and V satisfying

α (U) = β (V)

almost surely for some measure-preserving transformations α and β are called implicit dependence copulas. They were recently shown to coincide with the generalized Markov products of

C_{e, α}

and

C_{β, e}

with respect to a class of joining copulas

{(A_{t})}_{t \in [0, 1]}

. If

C_{e, α}

and

C_{β, e}

are not two-sided invertible, then most implicit dependence copulas, especially when

A_{t} \equiv Π

, are not extreme points in the class of copulas. For a given pair of left and right invertible copulas

C_{e, α}

and

C_{β, e}

, we characterize extreme implicit dependence copulas in terms of the extremality of the joining copulas in the class of subcopulas on a domain involving the invertible copulas. This result is then extended to the multivariate case.

对于某些保测度变换α和β，一致-(0,1)随机变量U和V几乎肯定满足α(U)=β(V)的copula称为隐相关copula。它们最近被证明与Ce，α和Cβ，e对一类连接copula (At)t∈[0,1]的广义马尔可夫积相吻合。如果Ce，α和Cβ，e不是双面可逆的，那么大多数隐式相关copula，特别是当At≡Π时，都不是copula类中的极值点。对于给定的一对左右可逆copulce，α和Cβ，e，我们用在包含可逆copulas的定义域上的次copulas类中的联结copulas的极值来表征极端隐相关copulas。然后将此结果推广到多变量情况。

引用次数: 0

Cauchy Graph Convolutional Networks 柯西图卷积网络

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-19 DOI: 10.1016/j.ijar.2025.109517

Taurai Muvunza , Yang Li , Ercan Engin Kuruoglu

A common approach to learning Bayesian networks involves specifying an appropriately chosen family of parameterized probability density such as Gaussian. However, the distribution of most real-life data is leptokurtic and may not necessarily be best described by a Gaussian process. In this work we introduce Cauchy Graphical Models (CGM), a class of multivariate Cauchy densities that can be represented as directed acyclic graphs with arbitrary network topologies, the edges of which encode linear dependencies between random variables. We develop CGLearn, the resultant algorithm for learning the structure and Cauchy parameters based on Minimum Dispersion Criterion (MDC). Experiments using simulated datasets on benchmark network topologies demonstrate the efficacy of our approach when compared to Gaussian Graphical Models (GGM). Most Graph Convolutional Neural Networks (GCN) process input graphs as ground-truth representations of node relationships, yet these graphs are constructed based on modeling assumptions and noisy data and their use may lead to suboptimal performance on downstream prediction tasks. We propose Cauchy GCN which leverages CGM to infer graph topology that depicts latent relationships between nodes. We evaluate the effectiveness and quality of the structural graphs learned by CGM, and demonstrate that Cauchy-GCN achieves superior performance compared to widely used graph construction methods.

学习贝叶斯网络的一种常用方法包括指定一个适当选择的参数化概率密度族，例如高斯。然而，大多数实际数据的分布是细峰的，不一定能用高斯过程来最好地描述。在这项工作中，我们引入了柯西图模型（CGM），这是一类多元柯西密度，可以表示为具有任意网络拓扑的有向无环图，其边缘编码随机变量之间的线性依赖关系。我们开发了CGLearn，这是基于最小色散准则（Minimum Dispersion Criterion， MDC）学习结构和柯西参数的结果算法。在基准网络拓扑上使用模拟数据集的实验表明，与高斯图形模型（GGM）相比，我们的方法是有效的。大多数图卷积神经网络（GCN）处理输入图作为节点关系的真值表示，然而这些图是基于建模假设和噪声数据构建的，它们的使用可能会导致下游预测任务的次优性能。我们提出了柯西GCN，它利用CGM来推断描述节点之间潜在关系的图拓扑。我们评估了CGM学习的结构图的有效性和质量，并证明了与广泛使用的图构造方法相比，Cauchy-GCN取得了更好的性能。

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引用次数: 0

Dynamic concept reduction methods based on local information 基于局部信息的动态概念约简方法

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-18 DOI: 10.1016/j.ijar.2025.109514

Mei-Zheng Li , Lei-Jun Li , Ju-Sheng Mi , Qian Hu

Knowledge reduction is one of the core research issues in formal concept analysis. As a new technique of knowledge reduction, concept reduction has received increasing attention recently. One typical method of calculating concept reducts is based on representative concept matrix (RC-matrix, for short), which can obtain all concept reducts. However, it is confronted with the following challenges: (1) before the construction of the RC-matrix, all concepts of the formal context need to be calculated, which is both time and space consuming; (2) there is a lot of redundant information in the constructed RC-matrix, which is not helpful to calculate the concept reducts; (3) when the data changes dynamically, the concept reducts need to be calculated for scratch. To address these issues, dynamic concept reduction methods based on local information are proposed in this paper. Firstly, the characteristics of the minimal elements (with respect to set inclusion) in the RC-matrix are analyzed, and all the minimal elements are directly labeled from the formal context; secondly, the advantage of local information is taken to construct each minimal elements of the RC-matrix, from which all the concept reducts can be obtained; besides, a new simplified version of RC-matrix, named as Type-I minimal RC-matrix, is further constructed to compute one concept reduct; and finally, when data dynamically changes, the connections between concept reducts of the original formal context and those of the new one are analyzed, consequently, two dynamic concept reduction algorithms are proposed.

知识约简是形式概念分析的核心研究问题之一。概念约简作为一种新的知识约简技术，近年来受到越来越多的关注。计算概念约简的一种典型方法是基于代表性概念矩阵（简称RC-matrix），它可以得到所有的概念约简。然而，它面临着以下挑战：(1)在构建rc矩阵之前，需要计算形式语境的所有概念，这既费时又耗空间；(2)构造的rc -矩阵中存在大量冗余信息，不利于概念约简的计算；(3)当数据发生动态变化时，需要对概念约简进行划痕计算。针对这些问题，本文提出了基于局部信息的动态概念约简方法。首先，分析了rc矩阵中最小元素（相对于集合包含）的特征，并从形式上下文直接标记了所有的最小元素；其次，利用局部信息的优势构造rc矩阵的每个最小元素，从中得到所有的概念约简；进一步构造了一个新的简化版rc -矩阵，命名为Type-I最小rc -矩阵，计算一个概念约简；最后，在数据动态变化的情况下，分析了原形式上下文的概念约简与新形式上下文的概念约简之间的联系，从而提出了两种动态概念约简算法。

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引用次数: 0

Structural reliability analysis for parameterized probability box based on efficient global optimization and dimension-reduction method 基于高效全局优化降维方法的参数化概率盒结构可靠性分析

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-18 DOI: 10.1016/j.ijar.2025.109513

Haibo Liu , Wen Lai , Weifeng Luo , Shufeng Zhang , Huichao Xie , Qiong Wang

In practical engineering, structural reliability analysis plays an important role in the safe operation of mechanical systems. The parameterized probability-box (p-box) model can effectively capture aleatory and epistemic uncertainties with flexibility and tunability to adapt to different conditions. This paper proposes a structural reliability analysis method for the problem with parameterized p-box based on efficient global optimization (EGO) and the univariate dimension reduction method (UDRM) to efficiently solve the upper and lower bounds of the failure probability of structures. First, the UDRM is used to calculate the origin moments of the performance function. Second, based on the results of the first four moments, the probability density function (PDF) of the performance function is constructed by the maximum entropy method (MEM) to compute the failure probability. Third, the EGO is utilized to obtain the upper and lower bounds of the failure probability of structures. Finally, the effectiveness of the proposed method is demonstrated through five numerical examples.

在实际工程中，结构可靠度分析对机械系统的安全运行起着重要的作用。参数化概率盒（p-box）模型能够有效地捕捉随机不确定性和认知不确定性，具有适应不同条件的灵活性和可调性。针对参数化p-box问题，提出了一种基于有效全局优化（EGO）和单变量降维法（UDRM）的结构可靠性分析方法，以有效求解结构失效概率的上界和下界。首先，使用UDRM计算性能函数的原点矩。其次，基于前4阶矩的结果，利用最大熵法（MEM）构造性能函数的概率密度函数（PDF），计算失效概率；第三，利用EGO求出结构失效概率的上界和下界。最后，通过5个算例验证了所提方法的有效性。

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引用次数: 0

Feasible strategies in three-way conflict analysis with three-valued ratings 基于三值评级的三向冲突分析中的可行策略

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-16 DOI: 10.1016/j.ijar.2025.109516

Jing Liu , Mengjun Hu , Guangming Lang

Most existing work on three-way conflict analysis has focused on trisecting agent pairs, agents, or issues. While these trisections lay the groundwork for understanding the nature of conflicts, further actions need to be formulated to address conflict resolution. One of the widely studied approaches is to construct feasible strategies. This paper aims to investigate feasible strategies from two perspectives of consistency and non-consistency. Particularly, we begin with computing the overall rating of a clique of agents based on positive and negative similarity degrees. Afterwards, considering the weights of both agents and issues, we propose weighted consistency and non-consistency measures, which are respectively used to identify the feasible strategies for a clique of agents. Algorithms are developed to identify feasible strategies, L-order feasible strategies, and the corresponding optimal ones. Finally, to demonstrate the practicality, effectiveness, and superiority of the proposed models, we apply them to two commonly used case studies on NBA labor negotiations and development plans for Gansu Province and conduct a sensitivity analysis on parameters and a comparative analysis with existing state-of-the-art conflict analysis approaches. The comparison results demonstrate that our conflict resolution models outperform the conventional approaches by unifying weighted agent-issue evaluation with consistency and non-consistency measures to enable the systematic identification of not only feasible strategies but also optimal solutions.

大多数关于三方冲突分析的现有工作都集中在三方代理对、代理或问题上。虽然这些三分法为理解冲突的性质奠定了基础，但需要制定进一步的行动来解决冲突。其中一个被广泛研究的方法是构建可行的策略。本文旨在从一致性和非一致性两个角度探讨可行策略。特别是，我们开始计算基于正相似度和负相似度的代理集团的总体评级。然后，考虑agent和问题的权重，提出加权一致性和非一致性度量，分别用于识别agent群的可行策略。提出了识别可行策略、l阶可行策略和相应的最优策略的算法。最后，为了证明所提出模型的实用性、有效性和优越性，我们将其应用于NBA劳资谈判和甘肃省发展计划的两个常用案例研究，并对参数进行敏感性分析，并与现有最先进的冲突分析方法进行比较分析。对比结果表明，我们的冲突解决模型通过统一具有一致性和非一致性度量的加权代理问题评估，不仅能够系统地识别可行策略，而且能够识别最优解决方案，从而优于传统方法。

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引用次数: 0

Computationally efficient variational-like approximations of possibilistic inferential models 计算效率的似变分的可能性推理模型近似

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-13 DOI: 10.1016/j.ijar.2025.109506

Leonardo Cella , Ryan Martin

Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite the IM framework's theoretical and foundational advantages, efficient computation is a challenge. This paper presents a simple yet powerful numerical strategy for approximating the IM's possibility contour, or at least its α-cut for a specified

α \in (0, 1)

. Our proposal starts with the specification of a parametric family that, in a certain sense, approximately covers the credal set associated with the IM's possibility measure. Akin to variational inference, we then propose to tune the parameters of that parametric family so that its

100 (1 - α) %

credible set roughly matches the IM contour's α-cut. This parametric α-cut matching strategy implies a full approximation to the IM's possibility contour at a fraction of the computational cost associated with previous strategies.

推理模型（IMs）提供可证明的可靠的、数据驱动的、可能性的统计推断。但是，尽管IM框架具有理论和基础优势，但高效计算仍然是一个挑战。本文给出了一种简单而强大的数值策略来逼近IM的可能性轮廓，或者至少是它的α-cut对于指定的α∈(0,1)。我们的建议从参数族的规范开始，在某种意义上，它大致涵盖了与IM的可能性度量相关的凭证集。类似于变分推理，我们建议调整该参数族的参数，使其100(1−α)%可信集大致匹配IM轮廓的α-cut。这种参数α-切匹配策略意味着在与先前策略相关的计算成本的一小部分上完全逼近IM的可能性轮廓。

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引用次数: 0

Conflict management in a distance to prototype-based evidential neural network 基于原型证据神经网络的远程冲突管理

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

International Journal of Approximate Reasoning

Pub Date : 2025-06-13 DOI: 10.1016/j.ijar.2025.109508

Dănuţ-Vasile Giurgi , Mihreteab Negash Geletu , Thomas Josso-Laurain , Maxime Devanne , Jean-Philippe Lauffenburger , Jean Dezert

Despite advances in integrating reasoning based on belief functions to generalise probabilistic representations, distance-to-prototype-based evidential deep neural networks are still emerging and require further consolidation. Existing studies in segmentation or classification tasks typically perform prior initialisation and do not address or mitigate the potential conflicts that may arise during fusion. This work investigates high-conflict scenarios within an evidential neural network for segmentation in autonomous driving, focusing on the distance-to-prototypes component, where prototypes, derived from feature maps, serve as sources of evidence and may yield contradictory information. Conflict is mitigated through parameter adjustments within the evidential reasoning, enhancing consistency before fusion. This enables more reliable data integration and a valid application of fusion rules and decision-making processes. The proposed rectification is validated on two prototype configurations of a deep evidential lidar-camera cross-fusion architecture, using two distance-based decision strategies and adapted metrics. The impact on the network's predictions is demonstrated through qualitative and quantitative results on road detection tasks with the KITTI dataset.

尽管基于信念函数的综合推理在推广概率表示方面取得了进展，但基于距离到原型的证据深度神经网络仍在兴起，需要进一步巩固。现有的分割或分类任务研究通常执行预先初始化，而没有解决或减轻融合过程中可能出现的潜在冲突。这项工作研究了自动驾驶中用于分割的证据神经网络中的高冲突场景，重点关注到原型的距离组件，其中原型来自特征地图，作为证据来源，可能会产生矛盾的信息。通过在证据推理中调整参数来缓解冲突，增强融合前的一致性。这可以实现更可靠的数据集成，并有效地应用融合规则和决策过程。采用两种基于距离的决策策略和自适应指标，在深度证据激光雷达-相机交叉融合架构的两种原型配置上验证了所提出的校正方法。通过KITTI数据集的道路检测任务的定性和定量结果，证明了对网络预测的影响。

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