International Journal of Approximate Reasoning最新文献

{"title":"Cross-worker joint modeling-based label integration for crowdsourcing","authors":"Can Pan,&nbsp;Liangxiao Jiang,&nbsp;Shanshan Si","doi":"10.1016/j.ijar.2025.109570","DOIUrl":"10.1016/j.ijar.2025.109570","url":null,"abstract":"<div><div>In crowdsourcing learning, label integration algorithms are applied to infer each instance's integrated label from its multiple noisy label set. Recent advancements have demonstrated that worker modeling is an effective approach to improving label integration's performance. In real-world crowdsourced scenarios, however, each worker often annotates a few instances only, leading to insufficient worker modeling. To address this issue, we propose a novel cross-worker joint modeling-based label integration (CJMLI) algorithm. Different from existing algorithms that focus on modeling individual workers solely, CJMLI exploits cross-worker joint modeling to effectively mitigate the impact of insufficient worker modeling. Specifically, we first employ majority voting to get initial integrated labels and then apply them to estimate worker qualities. Subsequently, for each instance, we randomly select a subset of workers to estimate its class membership probabilities and then generate a weighted instance for each class. Next, we use the weighted instances to train a classifier. This process is executed several times to get multiple classifiers. Finally, we use weighted majority voting to fuse their predicted labels to infer the final integrated label of each instance. Extensive experiments demonstrate that CJMLI significantly outperforms all its competitors.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109570"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104297","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":"Three types of internal conflict and its measurement in Dempster-Shafer theory","authors":"Andrey G. Bronevich ,&nbsp;Alexander E. Lepskiy","doi":"10.1016/j.ijar.2025.109572","DOIUrl":"10.1016/j.ijar.2025.109572","url":null,"abstract":"<div><div>In this paper, we extend the ideas of measuring conflict among belief functions based on solving Kantorovich problems to the case of measuring conflict within a belief function. We consider three possible interpretations of conflict-free information and propose the functionals for measuring conflict, which can be considered as counterparts of known functionals like the auto-conflict measure, the measure of dissonance, and the measure of logical inconsistency.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109572"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104438","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":"Optimizations of approximation operators in covering rough set theory","authors":"Shizhe Zhang ,&nbsp;Liwen Ma","doi":"10.1016/j.ijar.2025.109543","DOIUrl":"10.1016/j.ijar.2025.109543","url":null,"abstract":"<div><div>Classical rough set theory fundamentally requires upper and lower approximations to be definite sets for precise knowledge representation. However, a significant problem arises as many widely used approximation operators inherently produce rough approximations (with non-empty boundaries), contradicting this core theoretical intent and undermining practical applicability. To resolve this core discrepancy, we introduce stable approximation operators and stable sets, and develop an optimization method that transforms unstable operators into stable ones, ensuring definite approximations. This method includes detailing the optimization process with algorithmic implementation, analyzing the topological structure of resulting approximation spaces and connections between optimized operators, and enhancing computational efficiency via matrix-based computation. This work may strengthen rough set theory's foundation by bridging the gap between theory and practice while enhancing its scope for practical applications.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109543"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810436","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":"Three-way conflict analysis model via the best-worst method: Balancing subjective preferences and objective data on incomplete and dispersed systems","authors":"Junjie Zhu ,&nbsp;Qinghua Zhang ,&nbsp;Nanfang Luo ,&nbsp;Fan Liu ,&nbsp;Longjun Yin","doi":"10.1016/j.ijar.2025.109490","DOIUrl":"10.1016/j.ijar.2025.109490","url":null,"abstract":"<div><div>In real-world environments, different issues contribute to conflicts with varying weights. However, current conflict analysis weighting models face significant limitations when dealing with incomplete data and dispersed knowledge. Objective methods are sensitive to missing values and struggle to accurately capture authentic preferences, while subjective approaches lack systematic evaluation criteria, leading to substantial randomness in weight assignments. Therefore, a three-way conflict analysis model via the best-worst method is proposed, which is combined with a correlation coefficient method to balance subjective preferences and objective data. First, the trisection of agent pairs is derived through Bayesian minimum risk. Subsequently, a new conflict distance function is defined on the incomplete information system to provide a more precise measurement of conflict degrees. Then, for incomplete and dispersed information systems, a maximal coalition-based agent partitioning algorithm is designed, along with a new weighted voting mechanism to aggregate dispersed knowledge. Finally, the scientific transparency of the weighting process, as well as the robustness and feasibility of the model, are demonstrated through experimental analysis.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109490"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221952","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":"The language of Contextual Attribute Logics – Introduction and survey","authors":"Bernhard Ganter","doi":"10.1016/j.ijar.2025.109487","DOIUrl":"10.1016/j.ijar.2025.109487","url":null,"abstract":"<div><div>This article provides an introductory overview of Contextual Attribute Logic(s), a branch of Contextual Logic founded by Rudolf Wille. It presents a logic language that is based on mathematical logic but uses a different terminology to better serve the interpretative goal of Formal Concept Analysis.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109487"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204717","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":"Visual hallucination detection in large vision-language models via evidential conflict","authors":"Tao Huang ,&nbsp;Zhekun Liu ,&nbsp;Rui Wang ,&nbsp;Yang Zhang ,&nbsp;Liping Jing","doi":"10.1016/j.ijar.2025.109507","DOIUrl":"10.1016/j.ijar.2025.109507","url":null,"abstract":"<div><div>Despite the remarkable multimodal capabilities of Large Vision-Language Models (LVLMs), discrepancies often occur between visual inputs and textual outputs—a phenomenon we term visual hallucination. This critical reliability gap poses substantial risks in safety-critical Artificial Intelligence (AI) applications, necessitating a comprehensive evaluation benchmark and effective detection methods. Firstly, we observe that existing visual-centric hallucination benchmarks mainly assess LVLMs from a perception perspective, overlooking hallucinations arising from more advanced reasoning capabilities. We develop the Perception-Reasoning Evaluation Hallucination (PRE-HAL) dataset, which enables the systematic evaluation of both perception and reasoning capabilities of LVLMs across multiple visual semantics, such as instances, scenes, and relations. Comprehensive evaluation with this new benchmark exposed more visual vulnerabilities, particularly in the more challenging task of relation reasoning. To address this issue, we propose, to the best of our knowledge, the first Dempster-Shafer theory (DST)-based visual hallucination detection method for LVLMs through uncertainty estimation. This method aims to efficiently capture the degree of conflict in high-level features at the model inference phase. Specifically, our approach employs simple mass functions to mitigate the computational complexity of evidence combination on power sets. We conduct an extensive evaluation of state-of-the-art LVLMs, LLaVA-v1.5, mPLUG-Owl2, and mPLUG-Owl3, with the new PRE-HAL benchmark. Experimental results indicate that our method outperforms five baseline uncertainty metrics, achieving average AUROC improvements of 4% and 10% across three LVLMs. Notably, it exhibits remarkable robustness in scene perception tasks. These results validate that feature-level conflict analysis offers a scalable, cost-effective solution for enhancing LVLM trustworthiness. Our code is available at <span><span>https://github.com/HT86159/Evidential-Conflict</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109507"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291414","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":"A minimal base or a direct base? That is the question!","authors":"Jaume Baixeries ,&nbsp;Amedeo Napoli","doi":"10.1016/j.ijar.2025.109509","DOIUrl":"10.1016/j.ijar.2025.109509","url":null,"abstract":"<div><div>In this paper we revisit the problem of computing the closure of a set of attributes given a basis of dependencies or implications. This problem is of main interest in logics, in the relational database model, in lattice theory, and in Formal Concept Analysis as well. A basis of dependencies may have different characteristics, among which being “minimal”, e.g., the DG-Basis, or being “direct”, e.g., the Canonical-Direct Unit Basis and the <em>D</em>-base. Here we propose an extensive and experimental study of the impacts of minimality and directness on the closure algorithms. The results of the experiments performed on real and synthetic datasets are analyzed in depth, and suggest a different and fresh look at computing the closure of a set of attributes w.r.t. a basis of dependencies.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109509"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271893","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":"Knowledge granularity reduction for fuzzy relation decision systems","authors":"Qing Wang ,&nbsp;Xiuwei Gao ,&nbsp;Guilong Liu","doi":"10.1016/j.ijar.2025.109494","DOIUrl":"10.1016/j.ijar.2025.109494","url":null,"abstract":"<div><div>Knowledge granularity measures how effectively an equivalence relation can classify data within a knowledge base. It can also be used to describe the relationship between condition and decision attributes in decision tables. Knowledge granularity reduction is a significant type of attribute reduction for decision tables and its reduction algorithm for identifying all reducts was obtained in the past. In this paper, we extend such a reduction to relation decision systems and fuzzy relation decision systems, and obtain the corresponding reduction algorithm to identify all reducts. This algorithm unifies knowledge granularity reduction for decision tables, relation decision systems, and fuzzy relation decision systems. Finally, we use UCI and KRBM datasets to check the practicality and efficacy of our proposed algorithms.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109494"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221953","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":"Chain graphs structure learning given local background knowledge","authors":"Shujing Yang ,&nbsp;Fuyuan Cao ,&nbsp;Kui Yu ,&nbsp;Jiye Liang","doi":"10.1016/j.ijar.2025.109524","DOIUrl":"10.1016/j.ijar.2025.109524","url":null,"abstract":"<div><div>Chain graphs structure learning aims to identify and infer causal relations and symmetric association relations between variables in data. However, existing chain graphs structure learning algorithms cannot uniquely determine the causal relations from data among some variables due to the independence of these variables corresponding to multiple structures, making them only learn Markov equivalence classes of chain graphs. To alleviate this issue, we propose a <strong>C</strong>hain <strong>G</strong>raphs structure <strong>L</strong>earning algorithm <strong>G</strong>iven local background <strong>K</strong>nowledge (CGLGK). CGLGK initially learns the adjacencies and spouses of variables, constructs the skeleton of chain graphs using the adjacencies, corrects the connections between variables in the skeleton guided by local background knowledge, and orients the edges using the adjacencies and spouses to obtain the Markov equivalence classes of chain graphs. Next, CGLGK fuses local background knowledge with the learned Markov equivalence classes to obtain new knowledge. Finally, it utilizes the local valid orientation rule to orient edges within the Markov equivalence classes based on the updated knowledge, resulting in the final chain graphs structure. Meanwhile, we conducted the theoretical analysis to prove the correctness of CGLGK, and its effectiveness is verified by comparison with the classical and state-of-the-art algorithms on synthetic and real data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109524"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572380","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":"Joining copulas of extreme implicit dependence copulas","authors":"Noppawit Yanpaisan,&nbsp;Tippawan Santiwipanont,&nbsp;Songkiat Sumetkijakan","doi":"10.1016/j.ijar.2025.109518","DOIUrl":"10.1016/j.ijar.2025.109518","url":null,"abstract":"<div><div>Copulas of uniform-<span><math><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></math></span> random variables <em>U</em> and <em>V</em> satisfying <span><math><mi>α</mi><mo>(</mo><mi>U</mi><mo>)</mo><mo>=</mo><mi>β</mi><mo>(</mo><mi>V</mi><mo>)</mo></math></span> almost surely for some measure-preserving transformations <em>α</em> and <em>β</em> are called <em>implicit dependence copulas</em>. They were recently shown to coincide with the generalized Markov products of <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>e</mi><mo>,</mo><mi>α</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>β</mi><mo>,</mo><mi>e</mi></mrow></msub></math></span> with respect to a class of joining copulas <span><math><msub><mrow><mo>(</mo><msub><mrow><mi>A</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>)</mo></mrow><mrow><mi>t</mi><mo>∈</mo><mo>[</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow></msub></math></span>. If <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>e</mi><mo>,</mo><mi>α</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>β</mi><mo>,</mo><mi>e</mi></mrow></msub></math></span> are not two-sided invertible, then most implicit dependence copulas, especially when <span><math><msub><mrow><mi>A</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>≡</mo><mi>Π</mi></math></span>, are not extreme points in the class of copulas. For a given pair of left and right invertible copulas <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>e</mi><mo>,</mo><mi>α</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>β</mi><mo>,</mo><mi>e</mi></mrow></msub></math></span>, 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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109518"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490346","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}