International Journal of Approximate Reasoning最新文献_第3页

A two-player newsvendor game with competition on demand under ambiguity 歧义条件下按需竞争的二人报摊博弈

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-11 DOI: 10.1016/j.ijar.2025.109546

Andrea Cinfrignini , Silvia Lorenzini , Davide Petturiti

{"title":"A two-player newsvendor game with competition on demand under ambiguity","authors":"Andrea Cinfrignini , Silvia Lorenzini , Davide Petturiti","doi":"10.1016/j.ijar.2025.109546","DOIUrl":"10.1016/j.ijar.2025.109546","url":null,"abstract":"<div><div>We deal with a single period two-player newsvendor game where both newsvendors are assumed to be rational and risk-neutral, and to operate under ambiguity. Each newsvendor needs to choose his/her order quantity of the same perishable product, whose global market demand is modeled by a discrete random variable, endowed with a reference probability measure. Furthermore, the global market demand is distributed to newsvendors according to a proportional allocation rule. We model the uncertainty faced by each newsvendor with an individual <em>ϵ</em>-contamination of the reference probability measure, computed with respect to a suitable class of probability measures. The resulting <em>ϵ</em>-contamination model preserves the expected demand under the reference probability and is used to compute the individual lower expected profit as a Choquet expectation. Therefore, the optimization problem of each player reduces to settle the order quantity that maximizes his/her lower expected profit, given the opponent choice, which is a maximin problem. In the resulting game, we prove that a Nash equilibrium always exists, though it may not be unique. Finally, we provide a characterization of Nash equilibria in terms of best response functions.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109546"},"PeriodicalIF":3.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829499","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}

引用次数: 0

GTransformer: Multi-view functional granulation and self-attention for tabular data modeling GTransformer：用于表格数据建模的多视图功能粒化和自关注

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-08 DOI: 10.1016/j.ijar.2025.109547

Liang Liao , Yumin Chen , Yingyue Chen , Yiting Lin

{"title":"GTransformer: Multi-view functional granulation and self-attention for tabular data modeling","authors":"Liang Liao , Yumin Chen , Yingyue Chen , Yiting Lin","doi":"10.1016/j.ijar.2025.109547","DOIUrl":"10.1016/j.ijar.2025.109547","url":null,"abstract":"<div><div>To bridge the performance gap between deep learning models and tree ensemble methods in tabular data tasks, we propose GTransformer, a novel deep architecture that innovatively integrates granular computing and self-attention mechanisms. Our approach introduces a scalable granulation function set, from which diverse functions are randomly sampled to construct multi-view feature granules. These granules are aggregated into granule vectors, forming a multi-view functional granulation layer that provides comprehensive representations of tabular features from multiple perspectives. Subsequently, a Transformer encoder driven by granule sequences is employed to model deep interactions among features, with predictions generated via a hierarchical multilayer perceptron (MLP) classification head. Experiments on 12 datasets show that GTransformer achieves an average AUC of 92.9%, which is comparable to the 92.3% performance of LightGBM. Compared with the current mainstream deep model TabNet, the average AUC gain is 2.74%, with a 14.5% improvement on the Sonar dataset. GTransformer demonstrates strong robustness in scenarios with noise and missing data, especially on the Credit and HTRU2 datasets, where the accuracy decline is 24.73% and 17.03% less than that of MLP-Head respectively, further verifying its applicability in complex real-world application scenarios.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109547"},"PeriodicalIF":3.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829500","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}

引用次数: 0

Relative pre-reducts for computing the relative reducts of large data sets 用于计算大型数据集的相对约简的相对预约

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-07 DOI: 10.1016/j.ijar.2025.109544

Hajime Okawa , Yasuo Kudo , Tetsuya Murai

{"title":"Relative pre-reducts for computing the relative reducts of large data sets","authors":"Hajime Okawa , Yasuo Kudo , Tetsuya Murai","doi":"10.1016/j.ijar.2025.109544","DOIUrl":"10.1016/j.ijar.2025.109544","url":null,"abstract":"<div><div>In this paper, we introduce the concept of relative pre-reducts to derive the relative reducts from a large dataset. The relative reduct is considered a consistency-based attribute reduction method that is commonly utilized to extract concise subsets of condition attributes. Nonetheless, calculating all relative reducts necessitates substantial time and memory to build a discernibility matrix. In this research, we demonstrate that all relative pre-reducts can be computed using a simplified matrix referred to as the partial discernibility matrix, which can be readily converted into relative reducts. We also suggest employing a data partitioning approach to generate the discernibility matrix. This method alleviates the issue of an increased number of results for each partition. The outcomes from this technique yield the relative pre-reducts proposed in this study. Since our enhancements to the computation of relative reducts are independent of other advancements, they can be implemented in conjunction with existing methods. Experimental findings indicate that utilizing relative pre-reducts for computing relative reducts is efficient for large datasets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109544"},"PeriodicalIF":3.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829498","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}

引用次数: 0

Optimizations of approximation operators in covering rough set theory 覆盖粗糙集理论中近似算子的优化

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-08-07 DOI: 10.1016/j.ijar.2025.109543

Shizhe Zhang , Liwen Ma

引用次数: 0

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

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

{"title":"Distribution assessment-based multiple over-sampling with evidence fusion for imbalanced data classification","authors":"Hongpeng Tian , Zuowei Zhang , Zhunga Liu , Jingwei Zuo , Caixing Yang","doi":"10.1016/j.ijar.2025.109538","DOIUrl":"10.1016/j.ijar.2025.109538","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109538"},"PeriodicalIF":3.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829501","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}

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

International Journal of Approximate Reasoning Pub Date : 2025-08-05 DOI: 10.1016/j.ijar.2025.109536

Zhihui Zhang, Dun Liu, Rongping Shen

{"title":"A novel three-way based self-adaptive filtering model for sentiment analysis","authors":"Zhihui Zhang, Dun Liu, Rongping Shen","doi":"10.1016/j.ijar.2025.109536","DOIUrl":"10.1016/j.ijar.2025.109536","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109536"},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780439","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}

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

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

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

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

{"title":"Sensitivity analysis to unobserved confounding with copula-based normalizing flows","authors":"Sourabh Balgi , Marc Braun , Jose M. Peña , Adel Daoud","doi":"10.1016/j.ijar.2025.109531","DOIUrl":"10.1016/j.ijar.2025.109531","url":null,"abstract":"<div><div>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 <em>ρ</em>-GNF, where <span><math><mi>ρ</mi><mo>∈</mo><mo>[</mo><mo>−</mo><mn>1</mn><mo>,</mo><mo>+</mo><mn>1</mn><mo>]</mo></math></span> 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 <em>ρ</em>-GNF enables scholars to estimate the average causal effect (ACE) as a function of <em>ρ</em>, accounting for various confounding strengths. The output of the <em>ρ</em>-GNF is what we term the <span><math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>c</mi><mi>u</mi><mi>r</mi><mi>v</mi><mi>e</mi></mrow></msub></math></span>, which provides the bounds for the ACE given an interval of assumed <em>ρ</em> values. The <span><math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>c</mi><mi>u</mi><mi>r</mi><mi>v</mi><mi>e</mi></mrow></msub></math></span> 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 <em>ρ</em> 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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109531"},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810435","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}

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

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

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

International Journal of Approximate Reasoning Pub Date : 2025-07-29 DOI: 10.1016/j.ijar.2025.109535

P Sujithra , Sunil Mathew , J.N. Mordeson

{"title":"Optimizing connectivity in fuzzy graphs for resilient disaster response networks","authors":"P Sujithra , Sunil Mathew , J.N. Mordeson","doi":"10.1016/j.ijar.2025.109535","DOIUrl":"10.1016/j.ijar.2025.109535","url":null,"abstract":"<div><div>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 <span><math><mo>(</mo><mi>x</mi><mo>,</mo><mi>y</mi><mo>)</mo></math></span>-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 <span><math><mo>(</mo><mi>x</mi><mo>,</mo><mi>y</mi><mo>)</mo></math></span>-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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109535"},"PeriodicalIF":3.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749555","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}

引用次数: 0