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

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

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-07-17 DOI: 10.1016/j.ijar.2025.109529

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

{"title":"Maximal consistent blocks-based optimistic and pessimistic probabilistic rough fuzzy sets and their applications in three-way multiple attribute decision-making","authors":"Yan Sun , Bin Pang , Ju-Sheng Mi , Wei-Zhi Wu","doi":"10.1016/j.ijar.2025.109529","DOIUrl":"10.1016/j.ijar.2025.109529","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109529"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696895","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

Superhedging supermartingales Superhedging上鞅

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.ijar.2025.109567

C. Bender , S.E. Ferrando , K. Gajewski , A.L. González

引用次数: 0

Being Bayesian about learning Bayesian networks from hybrid data 成为贝叶斯就是从混合数据中学习贝叶斯网络

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-08-20 DOI: 10.1016/j.ijar.2025.109549

Marco Grzegorczyk

{"title":"Being Bayesian about learning Bayesian networks from hybrid data","authors":"Marco Grzegorczyk","doi":"10.1016/j.ijar.2025.109549","DOIUrl":"10.1016/j.ijar.2025.109549","url":null,"abstract":"<div><div>We develop a new Bayesian model to infer the structure of Bayesian networks from hybrid data, that is, data containing a mix of continuous (Gaussian) and discrete (categorical) variables. In line with state-of-the-art hybrid Bayesian network models, we do not allow discrete variables to have continuous parents. However, our new model differs from existing approaches by incorporating discrete variables through multivariate linear regression rather than mixture modeling. In our model, the continuous variables follow a multivariate Gaussian distribution with a shared covariance matrix, while the mean vector varies across different configurations.</div><div>As with all Bayesian network models, we use directed acyclic graphs (DAGs) to represent conditional dependency relations among the continuous variables. For our Gaussian distribution, this requires the covariance matrix to be consistent with the structure of the DAG. Our key idea is to apply multivariate linear regression, using the discrete variables as potential covariates to adjust the mean vector of the multivariate Gaussian distribution. Each continuous variable is associated with its own regression model and discrete parent set. Since the values of the discrete variables vary across observations, the mean vector becomes observation-specific.</div><div>This enables mean-adjustment of the continuous variables for their discrete parents while simultaneously inferring a Gaussian Bayesian network among them. In simulation studies, we compare our new model against two state-of-the-art hybrid Bayesian network models and demonstrate that both existing models have conceptual shortcomings, positioning our new hybrid Bayesian network model as a strong alternative.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109549"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886819","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 : 2025-12-01 Epub 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":"2025-12-01","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

Explainable granular fusion: Graph-embedded rectangular neighborhood rough sets for knowledge system convergence 可解释的颗粒融合：用于知识系统收敛的图嵌入矩形邻域粗糙集

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-08-28 DOI: 10.1016/j.ijar.2025.109561

Yigao Li, Weihua Xu

{"title":"Explainable granular fusion: Graph-embedded rectangular neighborhood rough sets for knowledge system convergence","authors":"Yigao Li, Weihua Xu","doi":"10.1016/j.ijar.2025.109561","DOIUrl":"10.1016/j.ijar.2025.109561","url":null,"abstract":"<div><div>With the development of Rough Set Theory (RST), many improved theories based on RST have emerged. Some of these theories have been applied in the field of feature selection, significantly improving its efficiency. However, they have not yet been widely used in multi-source information domains. This paper proposes a multi-source information fusion method based on Granular-Rectangular Neighborhood Rough Set (GRNRS) and graph theory. First, an improved algorithm based on GRNRS is proposed to evaluate the contribution of each information source to a classification task under a specific attribute. In this process, we provided rigorous theoretical proofs for the concepts and mechanisms used in the improved GRNRS. Meanwhile, the Pearson Correlation Coefficient (PCC) is used to assess the linear relationship between information sources. Then, by integrating the results of the improved GRNRS algorithm and PCC, the adjacency matrix of a graph is constructed. Finally, the preference value of each information source under a specific attribute is calculated based on the adjacency matrix. Information fusion under a specific attribute is achieved by selecting the information source with the highest preference value. Extensive experiments are conducted to analyze the impact of the algorithm's parameters on its final performance. Meanwhile, our method is compared with seven other information fusion algorithms using three metrics: classification accuracy, Average Quality (AQ), and runtime. Friedman and Nemenyi tests are conducted on the comparison results under the classification accuracy and AQ metrics, demonstrating that there are significant differences among the algorithms. The results demonstrate that the proposed algorithm is both time-efficient and effective.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109561"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922645","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

Self-supervised multi-level generative adversarial network data imputation algorithm 自监督多级生成对抗网络数据输入算法

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-08-27 DOI: 10.1016/j.ijar.2025.109553

Yi Xu , Shujuan Fang , Xuhui Xing

{"title":"Self-supervised multi-level generative adversarial network data imputation algorithm","authors":"Yi Xu , Shujuan Fang , Xuhui Xing","doi":"10.1016/j.ijar.2025.109553","DOIUrl":"10.1016/j.ijar.2025.109553","url":null,"abstract":"<div><div>Data missing has always been a challenging problem in machine learning. The Generative Adversarial Imputation Networks (GAIN) have been shown to outperform many existing solutions. However, in GAIN, because missing values lack ground truth as supervision, it is unable to construct reconstruction loss for missing values and can only judge the reasonableness of imputed values based on reconstruction loss of non-missing values and adversarial loss. From the perspective of granular computing, data has levels, and data at different levels of granularity encapsulates different knowledge. Therefore, based on granular computing, this paper proposes a self-supervised multi-level generative adversarial network data imputation algorithm (MGAIN). Firstly, multiple levels of data are constructed using nested feature set sequences. Then, GAIN is used to impute missing values at the coarsest granularity level, and the imputation results of missing values at the coarse granularity level are used as supervision for imputing missing values at the fine granularity level, constructing reconstruction loss for missing values at the fine granularity level. Finally, based on reconstruction loss of missing values, reconstruction loss of non-missing values, and adversarial loss, data at the finer granularity level is imputed. MGAIN imputes missing values level by level from the coarse granularity level to the fine granularity level to obtain more accurate imputation results. Experimental results validate the effectiveness of the proposed method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109553"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925202","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

On the optimality of coin-betting for mean estimation 基于均值估计的投币最优性研究

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-09-01 DOI: 10.1016/j.ijar.2025.109550

Eugenio Clerico

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 : 2025-12-01 Epub 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":"2025-12-01","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

Three-way conflict analysis: Issue reduct based on incomplete fuzzy value information 三向冲突分析：基于不完全模糊价值信息的问题缩减

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1016/j.ijar.2025.109568

Hai-Long Yang , Sheng Gao , Zhi-Lian Guo

{"title":"Three-way conflict analysis: Issue reduct based on incomplete fuzzy value information","authors":"Hai-Long Yang , Sheng Gao , Zhi-Lian Guo","doi":"10.1016/j.ijar.2025.109568","DOIUrl":"10.1016/j.ijar.2025.109568","url":null,"abstract":"<div><div>In the three-way conflict analysis (TWCA), certain core issues lead to the emergence, development, and resolution of conflicts. Issue reduct enables us to concentrate on key issues and more accurately identify the root causes of conflicts. Existing research primarily addresses issue reduct based on complete three-valued situation tables (TSTs), which have certain limitations. This paper discusses the issue reduct in TWCA based on incomplete fuzzy-valued situation tables (IFSTs). First, to deal with incomplete information, we introduce the Social Trust Network (STN) and the <em>K</em>-Nearest Neighbor (KNN) method, employing an iterative weighting method to fill in missing values. Second, by utilizing the matrix representation of relations among agents, we transform the relation matrix into constraint conditions and propose a recursive backtracking algorithm with pruning strategies to calculate conflict, neutrality, alliance, and global reducts. Finally, we use the development plan of the Gansu Provincial Government as a case study to illustrate the model's applicability and advantages through parameter and comparative analysis.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"187 ","pages":"Article 109568"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104296","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

Special issue on the Twelfth International Conference on Probabilistic Graphical Models (PGM 2024) 第十二届国际概率图模型会议特刊（PGM 2024）

IF 3 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-12-01 Epub Date: 2025-09-05 DOI: 10.1016/j.ijar.2025.109571

Silja Renooij, Johan Kwisthout, Janneke H. Bolt

引用次数: 0