International Journal of Approximate Reasoning最新文献

{"title":"Conflict management in a distance to prototype-based evidential neural network","authors":"Dănuţ-Vasile Giurgi ,&nbsp;Mihreteab Negash Geletu ,&nbsp;Thomas Josso-Laurain ,&nbsp;Maxime Devanne ,&nbsp;Jean-Philippe Lauffenburger ,&nbsp;Jean Dezert","doi":"10.1016/j.ijar.2025.109508","DOIUrl":"10.1016/j.ijar.2025.109508","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109508"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307169","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}

{"title":"Bayesianize fuzziness in the statistical analysis of fuzzy data","authors":"Antonio Calcagnì ,&nbsp;Przemysław Grzegorzewski ,&nbsp;Maciej Romaniuk","doi":"10.1016/j.ijar.2025.109495","DOIUrl":"10.1016/j.ijar.2025.109495","url":null,"abstract":"<div><div>Fuzzy data, prevalent in social sciences and other fields, capture uncertainties arising from subjective evaluations and measurement imprecision. Despite significant advancements in fuzzy statistics, a unified inferential regression-based framework remains undeveloped. Hence, we propose a novel approach for analyzing bounded fuzzy variables within a regression framework. Building on the premise that fuzzy data result from a process analogous to statistical coarsening, we introduce a conditional probabilistic approach that links observed fuzzy statistics (e.g., mode, spread) to the underlying, unobserved statistical model, which depends on external covariates. The inferential problem is addressed using Approximate Bayesian methods, mainly through a Gibbs sampler incorporating a quadratic approximation of the posterior distribution. Simulation studies and applications involving external validations are employed to evaluate the effectiveness of the proposed approach for fuzzy data analysis. By reintegrating fuzzy data analysis into a more traditional statistical framework, this work provides a significant step toward enhancing the interpretability and applicability of fuzzy statistical methods in many applicative contexts.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109495"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230545","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}

{"title":"Predicting graphical models based on functional data","authors":"Qiying Wu ,&nbsp;Huiwen Wang","doi":"10.1016/j.ijar.2025.109493","DOIUrl":"10.1016/j.ijar.2025.109493","url":null,"abstract":"<div><div>Graphical models are widely used to model complex relationships between variables in various fields. However, existing analysis methods focus primarily on scalar data and give little attention to addressing the challenges posed by nonscalar data, e.g., functional data, which are prevalent in many real-world applications. Additionally, most methods assume a static graphical model within the observed period, neglecting the dynamic changes that may occur over time. In this paper, we propose a novel method for predicting graphical models based on functional data. Our approach transforms functional data into finite-dimensional vectors via basis function expansion and cross-validation. We then establish a sequential prediction model for determining the correlation coefficient matrix of the decomposed data, thus accounting for the constraints imposed on the matrix via a transformation technique. Finally, we employ conditional independence tests to identify the edges of the predicted graphical model. We demonstrate the effectiveness of our method through extensive simulations and real data analyses. The results show that our method performs better than the competing methods in terms of prediction accuracy and provides valuable insights into the dynamic changes exhibited by a network. This work opens new possibilities for conducting graphical model analyses in various domains, particularly in terms of handling functional data and predicting dynamic relationships.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109493"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212380","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}

{"title":"FDACNet: Enhancing time-series classification with fuzzy feature and integrated self-attention and temporal convolution","authors":"Xiuwei Chen,&nbsp;Li Lai,&nbsp;Maokang Luo","doi":"10.1016/j.ijar.2025.109521","DOIUrl":"10.1016/j.ijar.2025.109521","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109521"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534999","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}

{"title":"Double Boolean algebras: Constructions, sub-structures and morphisms","authors":"Gael Tenkeu Kembang ,&nbsp;Yannick Léa Tenkeu Jeufack ,&nbsp;Etienne Romuald Temgoua Alomo ,&nbsp;Leonard Kwuida","doi":"10.1016/j.ijar.2025.109519","DOIUrl":"10.1016/j.ijar.2025.109519","url":null,"abstract":"<div><div>Double Boolean algebras are algebras <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder><mo>:</mo><mo>=</mo><mo>(</mo><mi>D</mi><mo>;</mo><mo>⊓</mo><mo>,</mo><mo>⊔</mo><mo>,</mo><mo>¬</mo><mo>,</mo><mo>⌟</mo><mo>,</mo><mo>⊥</mo><mo>,</mo><mo>⊤</mo><mo>)</mo></math></span> of type <span><math><mo>(</mo><mn>2</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>)</mo></math></span> introduced by Rudolf Wille to capture the equational theory of the algebra of protoconcepts. Every double Boolean algebra <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></math></span> contains two Boolean algebras: <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span>. Three main goals are achieved in this paper. First we characterize sub-algebras of a double Boolean algebra <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></math></span> as join sets of sub-algebras of the Boolean algebras <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span> and a subset of <span><math><mi>D</mi><mo>﹨</mo><msub><mrow><mi>D</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> (where <span><math><msub><mrow><mi>D</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>=</mo><msub><mrow><mi>D</mi></mrow><mrow><mo>⊓</mo></mrow></msub><mo>∪</mo><msub><mrow><mi>D</mi></mrow><mrow><mo>⊔</mo></mrow></msub></math></span>) satisfying certain conditions. Second, we characterize homomorphisms between two double Boolean algebras <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></math></span> and <span><math><munder><mrow><mi>E</mi></mrow><mo>_</mo></munder></math></span> by homomorphisms between the Boolean algebras <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>E</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span>, <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>E</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span> and maps between <span><math><mi>D</mi><mo>﹨</mo><msub><mrow><mi>D</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> and <em>E</em> satisfying certain conditions. Third, we give some tools to construct some classes of pure double Boolean algebras.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109519"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524340","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}

{"title":"On Fast arc-reversal","authors":"Cory J. Butz ,&nbsp;Anders L. Madsen ,&nbsp;Jhonatan S. Oliveira","doi":"10.1016/j.ijar.2025.109488","DOIUrl":"10.1016/j.ijar.2025.109488","url":null,"abstract":"<div><div><em>Fast arc-reversal</em> (FAR) was recently proposed as a new exact inference algorithm in discrete <em>Bayesian networks</em> (BNs), merging favourable features of <em>Arc-reversal</em> (AR) and <em>Variable elimination</em> (VE). AR constantly maintains a sub-BN structure when rendering a variable barren via arc reversals, often requiring more computational effort than VE, which sacrifices a sub-BN structure by directly eliminating a variable. It was formally established that FAR can <em>recover</em> a unique and sound sub-BN structure after consecutive variable eliminations. Experimental results on real-world benchmark networks empirically show an improvement in the average run-time and variance of FAR compared to AR. A novel method, called <em>d-contraction</em>, was suggested for graphically understanding FAR since FAR is not always the same as a sequence of arc reversals. Here, we extend this work by formally establishing that AR's sub-DAG is necessarily contained within FAR's sub-DAG. Unfortunately, neither FAR nor AR can guarantee the construction of minimal I-maps, although both methods may subsequently recover minimal I-mapness. Finally, it is shown how FAR improves Sum-Product network interpretability by relaxing a restriction on the elimination ordering used.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109488"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240487","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}

{"title":"Feasible strategies in three-way conflict analysis with three-valued ratings","authors":"Jing Liu ,&nbsp;Mengjun Hu ,&nbsp;Guangming Lang","doi":"10.1016/j.ijar.2025.109516","DOIUrl":"10.1016/j.ijar.2025.109516","url":null,"abstract":"<div><div>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, <em>L</em>-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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109516"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297784","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}

{"title":"Cut-elimination theorems for some logics associated with double Stone algebras","authors":"Martín Figallo,&nbsp;Juan S. Slagter","doi":"10.1016/j.ijar.2025.109526","DOIUrl":"10.1016/j.ijar.2025.109526","url":null,"abstract":"<div><div>A <em>double Stone algebra</em> is a Stone algebra whose dual lattice is also a Stone algebra. Logics that may be associated with double Stone algebras are based on bounded distributive lattices which are endowed with two negations: a Heyting negation (the pseudocomplement) and a Brouwer negation (the dual pseudocomplement) possibly satisfying some constraints. Different authors have studied the order-preserving logic associated with double Stone algebras. Recently, the four-valued character of this logic was exploited by providing a rough set semantics for it.</div><div>In this paper, we explore the proof-theoretical aspect of two logics associated with double Stone algebras, namely, the truth-preserving and the order-preserving logic, respectively. We provide sequent systems sound and complete for these logics and prove the cut-elimination theorem for both systems.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109526"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597360","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}

{"title":"Constructing intuitionistic neighborhood based on intuitionistic fuzzy sets for three-way clustering","authors":"Yu Xie ,&nbsp;Jilin Yang ,&nbsp;Yiyu Luo ,&nbsp;Xianyong Zhang ,&nbsp;Junfang Luo","doi":"10.1016/j.ijar.2025.109512","DOIUrl":"10.1016/j.ijar.2025.109512","url":null,"abstract":"<div><div>Three-way clustering assigns highly uncertain samples to the boundary domains, effectively addressing the problem of misclassification caused by data uncertainty. In numerical attribute information systems, neighborhood rough sets can effectively capture the indiscernibility relations between objects. However, the conventional neighborhood relation suffers from a one-size-fits-all issue due to the fixed neighborhood radius. To solve these issues, we propose an intuitionistic neighborhood and construct a corresponding three-way clustering model. Specifically, we first capture the dual nature and uncertainty of neighborhood relations through the construction of the intuitionistic neighborhood. Then we construct a three-way clustering model with dual and single evaluation functions based on intuitionistic neighborhoods. Finally, we adaptively obtain the optimal threshold pairs by maximizing the clustering effectiveness index. Experiments conducted on twelve datasets demonstrate that our proposed method outperforms baseline methods, showing superior capability in handling the inherent uncertainty in information systems.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109512"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307170","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}

{"title":"Dynamic concept reduction methods based on local information","authors":"Mei-Zheng Li ,&nbsp;Lei-Jun Li ,&nbsp;Ju-Sheng Mi ,&nbsp;Qian Hu","doi":"10.1016/j.ijar.2025.109514","DOIUrl":"10.1016/j.ijar.2025.109514","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109514"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472111","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}