International Journal of Approximate Reasoning最新文献

{"title":"Three-way clustering based on the graph of local density trend","authors":"Haifeng Yang ,&nbsp;Weiqi Wang ,&nbsp;Jianghui Cai ,&nbsp;Jie Wang ,&nbsp;Yating Li ,&nbsp;Yaling Xun ,&nbsp;Xujun Zhao","doi":"10.1016/j.ijar.2025.109422","DOIUrl":"10.1016/j.ijar.2025.109422","url":null,"abstract":"<div><div>Three-way clustering demonstrates its unique advantages in dealing with the issues of information ambiguity and unclear boundaries present in real-world datasets. The core and boundary region in the data are identified as key features of cluster analysis. Typically, data is segmented into three regions based on a set of predetermined global thresholds, a common practice in three-way clustering. However, this method, which relies on global thresholds, often overlooks the intrinsic distribution patterns within the dataset and determining these thresholds a priori can be quite challenging. In this paper, we propose a three-way clustering method based on the graph of local density trend (3W-GLDT). Specifically, the algorithm first uses a density-decreasing strategy to build subgraphs and divide the core region data. Then, the unreasonable connection is corrected by using isolated forest, which increases the number of core points and enlarges the distribution range of core points. Next, a three-way allocation strategy is proposed, which fully considers the degree of local aggregation of subgraphs and the natural domain information of each data object to ensure the correct allocation. Finally, the proposed algorithm is compared with 8 different clustering methods on 8 synthetic datasets and 10 UCI real datasets. The experimental results show that the 3W-GLDT algorithm has good performance and clustering results.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109422"},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684390","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":"Interval three-way decision model based on data envelopment analysis and prospect theory","authors":"Xianwei Xin ,&nbsp;Xiao Yu ,&nbsp;Tao Li ,&nbsp;Zhanao Xue","doi":"10.1016/j.ijar.2025.109424","DOIUrl":"10.1016/j.ijar.2025.109424","url":null,"abstract":"<div><div>Since the three-way decision theory was proposed, two paradigms, “narrow sense” and “broad sense”, have gradually evolved, each demonstrating unique advantages in handling multi-granularity information and uncertainty analysis. These approaches provide a systematic theoretical framework for solving complex decision-making problems. However, the traditional three-way decision model has limitations in multi-input-output scenarios, and the current method is still insufficient in characterizing the risk attitudes and psychological characteristics of decision-makers. To address these challenges, this paper proposes an interval three-way decision model based on Data Envelopment Analysis (DEA) and Prospect theory for multi-input-output decision problems. First, we define a many-valued decision information system based on DEA, using benefit scores from various orientations as decision attributes to formulate decision strategies. Second, to mitigate the subjective bias introduced by reference points in Prospect theory, we introduce triangular fuzzy reference points that account for interval uncertainty. Additionally, we propose a calculation method for the multi-input-output interval membership function of Decision-Making Units (DMUs) and a construction method for the value function. Comprehensive decision rules are derived by calculating the overall prospect value. Finally, the effectiveness of the proposed model is validated using a series of experiments across multiple datasets, with comparisons to over ten existing methods. The results indicate that the model achieves competitive performance in terms of classification accuracy and decision-making efficiency, demonstrating its strengths in addressing uncertain multi-input-output decision problems while incorporating decision-makers' risk preferences in an interval environment.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109424"},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684270","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":"Surprisingly popular-based multivariate conceptual knowledge acquisition method","authors":"Xianwei Xin ,&nbsp;Shiting Yuan ,&nbsp;Tao Li ,&nbsp;Zhanao Xue ,&nbsp;Chenyang Wang","doi":"10.1016/j.ijar.2025.109419","DOIUrl":"10.1016/j.ijar.2025.109419","url":null,"abstract":"<div><div>Formal Concept Analysis (FCA) plays a crucial role in uncertain artificial intelligence by revealing specialization and generalization relationships among concepts. Unlike artificial neural network methods, concepts are fundamental to human cognition and learning, making knowledge acquisition and decision-making methods highly significant, especially when influenced by data and cognition. However, existing FCA models and extensions predominantly emphasize data-driven approaches, often disregarding the cognitive attributes of individual learners. This paper introduces a novel method for acquiring diverse conceptual knowledge and a cognition-enhanced decision-making approach based on the Surprisingly Popular (SP) method. Initially, it defines a new multivariate relational formal context and its corresponding decision-making method to facilitate a more sophisticated exploration of uncertain information. Additionally, it presents a method for quantifying the similarity of multivariate concepts. The SP approach is then integrated to identify the core and peripheral multivariate concepts within the multivariate concept lattice. Furthermore, the paper develops a multivariate cognitive decision-making method and presents the corresponding algorithm. Finally, instance analysis is conducted on the UCI dataset to compare the proposed method with state-of-the-art models. The results indicate that the proposed model effectively uncovers core and peripheral concepts within uncertain information by incorporating human cognitive decision processes.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109419"},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684389","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":"Improved evidential three-way decisions in incomplete multi-scale information systems","authors":"Rui Li ,&nbsp;Chao Zhang ,&nbsp;Deyu Li ,&nbsp;Wentao Li ,&nbsp;Jianming Zhan","doi":"10.1016/j.ijar.2025.109417","DOIUrl":"10.1016/j.ijar.2025.109417","url":null,"abstract":"<div><div>Granular computing, by simulating human thought processes, provides a paradigm for solving complex decision-making problems. The three-way decision is a key component of granular computing. Compared to traditional decision-making methodologies, it introduces “deferred decision-making”, allowing for multi-granularity exploration of alternatives. This process gradually refines the granularity of alternatives, leading to a transition between acceptance and rejection. Moreover, data structures in the real world are typically multi-granular, multi-level, and incomplete. Compared to single-scale information systems, incomplete multi-scale information systems provide richer decision foundations by mapping information to different levels. Additionally, they allow for more precise and flexible decision-making by integrating attribute information from different levels at a specific granularity, depending on requirements. Therefore, this paper seeks to present a three-way multi-scale decision-making methodology under incomplete environments. First, an integration methodology under incomplete multi-scale information systems by using the best-worst method is built, which comprehensively considers the importance of each scale of attributes. Second, the grey relation analysis calculation is integrated into the technique for order preference by similarity to ideal solution methodology to obtain the score of alternatives, serving as the conditional probability of three-way decisions, which compensates for the problem of Euclidean distance failures due to the correlation among indicators. Third, according to the evidence theory and the enhanced belief Jensen-Sharma-Mittal divergence, the thresholds of three-way decisions are fused, improving the classification efficiency of three-way decisions for alternatives. Finally, the effectiveness of the methodology established in this paper is validated using the campus environment evaluation data set collected through the Questionnaire Star from Shanxi University, China.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109417"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680103","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":"Matrix-based local multigranulation reduction for covering decision information systems","authors":"Tao Jiang ,&nbsp;Yan-Lan Zhang","doi":"10.1016/j.ijar.2025.109415","DOIUrl":"10.1016/j.ijar.2025.109415","url":null,"abstract":"<div><div>Attribute reduction has become an essential step in pattern recognition and machine learning tasks. As an extension of the classical rough set, the covering rough set has garnered considerable attention in both theory and application. A matrix-based method for computing local covering optimistic approximation sets and local optimistic multigranulation reductions based on covering rough set in covering decision information systems (CDISs) is proposed in this paper. Firstly, we introduce a matrix representation along with its associated operations to compute the local covering optimistic approximation sets and the local positive regions of the CDISs. Subsequently, local optimistic discernibility matrices and local optimistic discernibility functions are constructed for the CDISs. By performing disjunction and conjunction operations on these local optimistic discernibility matrices, all local optimistic multigranulation reductions of the CDISs can be accurately obtained. In addition, an algorithm is developed using the local optimistic discernibility matrix to compute a suboptimal minimal local optimistic multigranulation reduction. Finally, to verify the effectiveness and feasibility of the proposed method, numerical experiments are conducted on 6 UCI datasets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109415"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654631","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 novel axiomatic approach to L-valued rough sets within an L-universe via inner product and outer product of L-subsets","authors":"Lingqiang Li,&nbsp;Qiu Jin","doi":"10.1016/j.ijar.2025.109416","DOIUrl":"10.1016/j.ijar.2025.109416","url":null,"abstract":"<div><div>The fuzzy rough approximation operator serves as the cornerstone of fuzzy rough set theory and its practical applications. Axiomatization is a crucial approach in the exploration of fuzzy rough sets, aiming to offer a clear and direct characterization of fuzzy rough approximation operators. Among the fundamental tools employed in this process, the inner product and outer product of fuzzy sets stand out as essential components in the axiomatization of fuzzy rough sets. In this paper, we will develop the axiomatization of a comprehensive fuzzy rough set theory, that is, the so-called <em>L</em>-valued rough sets with an <em>L</em>-set serving as the foundational universe (referred to as the <em>L</em>-universe) for defining <em>L</em>-valued rough approximation operators, where <em>L</em> typically denotes a GL-quantale. Firstly, we give the notions of inner product and outer product of two <em>L</em>-subsets within an <em>L</em>-universe and examine their basic properties. It is shown that these notions are extensions of the corresponding notion of fuzzy sets within a classical universe. Secondly, leveraging the inner product and outer product of <em>L</em>-subsets, we respectively characterize <em>L</em>-valued upper and lower rough approximation operators generated by general, reflexive, transitive, symmetric, Euclidean, and median <em>L</em>-value relations on <em>L</em>-universe as well as their compositions. Finally, utilizing the provided axiomatic characterizations, we present the precise examples for the least and largest equivalent <em>L</em>-valued upper and lower rough approximation operators. Notably, many existing axiom characterizations of fuzzy rough sets within classical universe can be viewed as direct consequences of our findings.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109416"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654632","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":"Semi-supervised hierarchical multi-label classifier based on local information","authors":"Jonathan Serrano-Pérez ,&nbsp;L. Enrique Sucar","doi":"10.1016/j.ijar.2025.109411","DOIUrl":"10.1016/j.ijar.2025.109411","url":null,"abstract":"<div><div>Scarcity of labeled data is a common problem in supervised classification, since hand-labeling can be time consuming, expensive or hard to label; on the other hand, large amounts of unlabeled information can be found. The problem of scarcity of labeled data is even more notorious in hierarchical classification, because the data of a node is split among its children, which results in few instances associated to the deepest nodes of the hierarchy. In this work it is proposed the <em>semi-supervised hierarchical multi-label classifier based on local information</em> (SSHMC-BLI) which can be trained with labeled and unlabeled data to perform hierarchical classification tasks. The method can be applied to any type of hierarchical problem, here we focus on the most difficult case: hierarchies of DAG type, where the instances can be associated to multiple paths of labels which can finish in an internal node. SSHMC-BLI builds pseudo-labels for each unlabeled instance from the paths of labels of its labeled neighbors, while it considers whether the unlabeled instance is similar to its neighbors. Experiments on 12 challenging datasets from functional genomics show that making use of unlabeled along with labeled data can help to improve the performance of a supervised hierarchical classifier trained only on labeled data, even with statistical significance.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109411"},"PeriodicalIF":3.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619890","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":"Characterizations for union and intersection on non-normal membership functions of type-2 fuzzy sets","authors":"Zhi-qiang Liu ,&nbsp;Jingxin Liu","doi":"10.1016/j.ijar.2025.109414","DOIUrl":"10.1016/j.ijar.2025.109414","url":null,"abstract":"<div><div>In this work, we mainly investigate set operations for type-2 fuzzy sets. To be more exact, we present several algorithms under the left continuous t-norms that compute the join and meet operations of the non-normal convex secondary membership functions of type-2 fuzzy sets, and give some properties of operations that would enhance the application of fuzzy logic connectives. We anticipate that these algorithms can be applied to type-2 fuzzy logic systems as well as several fields of soft computing that tackle logical operations in type-2 fuzzy sets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109414"},"PeriodicalIF":3.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609672","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":"Hyperspace approach to relation-based neighborhood operators","authors":"Marian Przemski","doi":"10.1016/j.ijar.2025.109404","DOIUrl":"10.1016/j.ijar.2025.109404","url":null,"abstract":"<div><div>Recently, new, subsequent versions of neighborhoods have been defined based on the concept of relation-based neighborhoods introduced by Y.Y. Yao. This article proposes a unified concept for investigations of such neighborhoods. This work presents the notion of hyper-neighborhood, which enables the investigation of the neighborhoods from the universe's perspective. As a result, we drive multiple equivalent characterizations of the types of neighborhoods that enable us to compare them and indicate the new, missing kinds of neighborhoods. Moreover, many kinds of neighborhoods defined in the literature on the issue proved to be identical. In particular, none of the types of recently defined so-called subset neighborhoods is new.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109404"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579322","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":"Evidential time-to-event prediction with calibrated uncertainty quantification","authors":"Ling Huang ,&nbsp;Yucheng Xing ,&nbsp;Swapnil Mishra ,&nbsp;Thierry Denœux ,&nbsp;Mengling Feng","doi":"10.1016/j.ijar.2025.109403","DOIUrl":"10.1016/j.ijar.2025.109403","url":null,"abstract":"<div><div>Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. However, this task is more challenging than standard regression problems due to the presence of censored observations. Additionally, the lack of confidence assessment, model robustness, and prediction calibration raises concerns about the reliability of predictions. To address these challenges, we propose an evidential regression model specifically designed for time-to-event prediction. Our approach computes a degree of belief for the event time occurring within a time interval, without any strict distribution assumption. Meanwhile, the proposed model quantifies both epistemic and aleatory uncertainties using Gaussian Random Fuzzy Numbers and belief functions, providing clinicians with uncertainty-aware survival time predictions. Experimental evaluations using simulated and real-world survival datasets highlight the potential of our approach for enhancing clinical decision-making in survival analysis.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109403"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}