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

Multi-view outlier detection based on multi-granularity fusion of fuzzy rough granules

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-02 DOI: 10.1016/j.ijar.2025.109402

Siyi Qiu , Yuefei Wang , Zixu Wang , Jinyan Cao , Xi Yu

{"title":"Multi-view outlier detection based on multi-granularity fusion of fuzzy rough granules","authors":"Siyi Qiu , Yuefei Wang , Zixu Wang , Jinyan Cao , Xi Yu","doi":"10.1016/j.ijar.2025.109402","DOIUrl":"10.1016/j.ijar.2025.109402","url":null,"abstract":"<div><div>In recent years, multi-view data has seen widespread application across various fields, presenting both opportunities and challenges due to its complex distribution across different views. Detecting outliers in such heterogeneous data has become a significant research problem. Existing multi-view outlier detection methods often rely on clustering assumptions, pairwise constraints between views, and a focus on learning consensus information, which overlook the inherent differences across views. To address the aforementioned issues, this paper proposes an outlier detection method based on the fusion of multi-granularity fuzzy rough information (MGFMOD). The method calculates a multi-granularity similarity matrix using fuzzy similarity relationships, combines similarity matrices from different granularities to form an upper approximation matrix, and constructs fused upper approximation granules to detect attribute anomalies. Neighbor domain probabilistic mapping is then employed to unify neighborhood relationships across views, allowing the analysis of both consistency and distribution differences to capture class outliers. Additionally, this paper employs a novel coarse-to-fine approximation method to construct the upper approximation matrix, further improving the accuracy of attribute outlier detection. Experimental results on multiple public datasets demonstrate that the proposed method generally outperforms existing multi-view outlier detection methods in terms of detection accuracy and robustness.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109402"},"PeriodicalIF":3.2,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636801","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

Multiindistinguishability operators 多区分性算子

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-28 DOI: 10.1016/j.ijar.2025.109401

D. Boixader, J. Recasens

引用次数: 0

DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-27 DOI: 10.1016/j.ijar.2025.109400

Loïc Guiziou , Emmanuel Ramasso , Sébastien Thibaud , Sébastien Denneulin

引用次数: 0

Soft computing for the posterior of a matrix t graphical network

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-24 DOI: 10.1016/j.ijar.2025.109397

Jason Pillay , Andriette Bekker , Johannes Ferreira , Mohammad Arashi

{"title":"Soft computing for the posterior of a matrix t graphical network","authors":"Jason Pillay , Andriette Bekker , Johannes Ferreira , Mohammad Arashi","doi":"10.1016/j.ijar.2025.109397","DOIUrl":"10.1016/j.ijar.2025.109397","url":null,"abstract":"<div><div>Modeling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments. Noisy data necessitates the probabilistic characterization of these networks using matrix variate models. Denoising network data using a Bayesian approach is not common in surveyed literature. Therefore, this paper adopts the Bayesian viewpoint and introduces a new version of the matrix variate t graphical network. This model's prior beliefs rely on the matrix variate gamma distribution to handle the noise process flexibly; from a statistical learning viewpoint, such a theoretical consideration benefits the comprehension of structures and processes that cause network-based noise in data as part of machine learning and offers real-world interpretation. A proposed Gibbs algorithm is provided for computing and approximating the resulting posterior probability distribution of interest to assess the considered model's network centrality measures. Experiments with synthetic and real-world stock price data are performed to validate the proposed algorithm's capabilities and show that this model has wider flexibility than the model proposed by <span><span>[13]</span></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109397"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508698","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}

引用次数: 0

Fuzzy time series analysis: Expanding the scope with fuzzy numbers

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-21 DOI: 10.1016/j.ijar.2025.109387

Hugo J. Bello , Manuel Ojeda-Hernández , Domingo López-Rodríguez , Carlos Bejines

引用次数: 0

Asymptotic efficiency of inferential models and a possibilistic Bernstein–von Mises theorem

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-20 DOI: 10.1016/j.ijar.2025.109389

Ryan Martin, Jonathan P. Williams

{"title":"Asymptotic efficiency of inferential models and a possibilistic Bernstein–von Mises theorem","authors":"Ryan Martin, Jonathan P. Williams","doi":"10.1016/j.ijar.2025.109389","DOIUrl":"10.1016/j.ijar.2025.109389","url":null,"abstract":"<div><div>The inferential model (IM) framework offers an alternative to the classical probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. A key distinction is that classical uncertainty quantification takes the form of precise probabilities and offers only limited large-sample validity guarantees, whereas the IM's uncertainty quantification is imprecise in such a way that exact, finite-sample valid inference is possible. But are the IM's imprecision and finite-sample validity compatible with statistical efficiency? That is, can IMs be both finite-sample valid and asymptotically efficient? This paper gives an affirmative answer to this question via a new possibilistic Bernstein–von Mises theorem that parallels a fundamental Bayesian result. Among other things, our result shows that the IM solution is efficient in the sense that, asymptotically, its credal set is the smallest that contains the Gaussian distribution with variance equal to the Cramér–Rao lower bound. Moreover, a corresponding version of this new Bernstein–von Mises theorem is presented for problems that involve the elimination of nuisance parameters, which settles an open question concerning the relative efficiency of profiling-based versus extension-based marginalization strategies.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109389"},"PeriodicalIF":3.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480018","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

Maximal hypercliques search based on concept-cognitive learning

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-20 DOI: 10.1016/j.ijar.2025.109386

Jiawei Wang , Fei Hao , Jie Gao , Li Zou , Zheng Pei

{"title":"Maximal hypercliques search based on concept-cognitive learning","authors":"Jiawei Wang , Fei Hao , Jie Gao , Li Zou , Zheng Pei","doi":"10.1016/j.ijar.2025.109386","DOIUrl":"10.1016/j.ijar.2025.109386","url":null,"abstract":"<div><div>Maximal hyperclique search, focused on finding the largest hypernode subsets in a hypergraph such that every combination of <em>r</em> nodes in these subsets forms a hyperedge, is a fundamental problem in hypergraph mining. However, compared to traditional graphs, the combinatorial explosion of hyperedges significantly increases the complexity of enumeration, especially as the <em>r</em>-value and the number of hypernodes grow, rapidly expanding the search space. Moreover, overlapping hyperedges in dense hypergraphs lead to substantial redundant checks, further exacerbating search inefficiency, making traditional methods inadequate for large-scale hypergraphs. To tackle these challenges, this paper proposes a novel approach MHSC that handles the maximal hyperclique search task in <em>r</em>-uniform hypergraph based on concept-cognitive learning. Concept-cognitive learning refers to the process of understanding and structuring knowledge through the formation of concepts and their interrelationships. Technically, the hypernode-neighbor structure of the hypergraph is first expressed as a formal context, and the required concepts are generated using the concept lattice algorithm. Based on the shared relationships between hypernodes represented by the hyperedges, a series of theorems are proposed to prune hypernodes that cannot form maximal hypercliques within the sets of 1-intent and 2-intent concepts, thereby narrowing the search space and reducing redundant computations. Furthermore, an optimization method termed MHSC+ is introduced. Extensive experiments conducted on both test datasets and real-world datasets demonstrate the effectiveness, efficiency, and applicability of the proposed algorithm.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109386"},"PeriodicalIF":3.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474846","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

Assessing inference to the best explanation posteriors for the estimation of economic agent-based models

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-20 DOI: 10.1016/j.ijar.2025.109388

Francesco De Pretis , Aldo Glielmo , Jürgen Landes

引用次数: 0

A dialectical formalisation of preferred subtheories reasoning under resource bounds

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-17 DOI: 10.1016/j.ijar.2025.109385

Kees van Berkel , Marcello D'Agostino , Sanjay Modgil

{"title":"A dialectical formalisation of preferred subtheories reasoning under resource bounds","authors":"Kees van Berkel , Marcello D'Agostino , Sanjay Modgil","doi":"10.1016/j.ijar.2025.109385","DOIUrl":"10.1016/j.ijar.2025.109385","url":null,"abstract":"<div><div><em>Dialectical Classical Argumentation</em> (Dialectical <em>Cl-Arg</em>) has been shown to satisfy rationality postulates under resource bounds. In particular, the consistency and non-contamination postulates are satisfied despite dropping the assumption of logical omniscience and the consistency and subset minimality checks on arguments' premises that are deployed by standard approaches to <em>Cl-Arg</em>. This paper studies Dialectical <em>Cl-Arg</em>'s formalisation of Preferred Subtheories (<em>PS</em>) non-monotonic reasoning under resource bounds. The contribution of this paper is twofold. First, we establish soundness and completeness for Dialectical <em>Cl-Arg</em>'s credulous consequence relation under the <em>preferred</em> semantics and credulous <em>PS</em> consequences. This result paves the way for the use of argument game proof theories and dialogues that establish membership of arguments in admissible (and so preferred) extensions, and hence the credulous <em>PS</em> consequences of a belief base. Second, we refine the non-standard characteristic function for Dialectical <em>Cl-Arg</em>, and use this refined function to show soundness for Dialectical <em>Cl-Arg</em> consequences under the grounded semantics and resource-bounded sceptical <em>PS</em> consequence. We provide a counterexample that shows that completeness does not hold. However, we also show that the grounded consequences defined by Dialectical <em>Cl-Arg</em> strictly subsume the grounded consequences defined by standard <em>Cl-Arg</em> formalisations of <em>PS</em>, so that we recover sceptical <em>PS</em> consequences that one would intuitively expect to hold.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109385"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429220","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}

引用次数: 0

An unsupervised feature extraction and fusion framework for multi-source data based on copula theory

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-02-13 DOI: 10.1016/j.ijar.2025.109384

Xiuwei Chen, Li Lai, Maokang Luo

{"title":"An unsupervised feature extraction and fusion framework for multi-source data based on copula theory","authors":"Xiuwei Chen, Li Lai, Maokang Luo","doi":"10.1016/j.ijar.2025.109384","DOIUrl":"10.1016/j.ijar.2025.109384","url":null,"abstract":"<div><div>With the development of big data technology, people are increasingly facing the challenge of dealing with massive amounts of multi-source or multi-sensor data. Therefore, it becomes crucial to extract valuable information from such data. Information fusion techniques provide effective solutions for handling multi-source data and can be categorized into three levels: data-level fusion, feature-level fusion, and decision-level fusion. Feature-level fusion combines features from multiple sources to create a consolidated feature, enhancing information richness. This paper proposes an unsupervised feature extraction and fusion method for multi-source data that utilizes the R-Vine copula, denoted as CF. The method starts by performing kernel density estimation to extract each data source's marginal density and distribution. Next, the maximum spanning tree is employed to select a vine structure for each attribute, and the corresponding copulas are chosen using maximum likelihood estimation and the AIC criterion. The joint probability density of each attribute across all information sources can be obtained by utilizing the relevant vine structure and copulas, serving as the final fusion feature. Finally, the proposed method is evaluated on eighteen simulated datasets and six real datasets. The results indicate that compared to several state-of-the-art fusion methods, the CF method can significantly enhance the classification accuracy of popular classifiers such as KNN, SVM, and Logistic Regression.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109384"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419648","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