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

Semi-supervised hierarchical multi-label classifier based on local information

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-10 DOI: 10.1016/j.ijar.2025.109411

Jonathan Serrano-Pérez , L. Enrique Sucar

{"title":"Semi-supervised hierarchical multi-label classifier based on local information","authors":"Jonathan Serrano-Pérez , 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}

引用次数: 0

Characterizations for union and intersection on non-normal membership functions of type-2 fuzzy sets

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-10 DOI: 10.1016/j.ijar.2025.109414

Zhi-qiang Liu , Jingxin Liu

引用次数: 0

Hyperspace approach to relation-based neighborhood operators

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-07 DOI: 10.1016/j.ijar.2025.109404

Marian Przemski

引用次数: 0

Evidential time-to-event prediction with calibrated uncertainty quantification

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-04 DOI: 10.1016/j.ijar.2025.109403

Ling Huang , Yucheng Xing , Swapnil Mishra , Thierry Denœux , Mengling Feng

引用次数: 0

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