International Journal of Approximate Reasoning最新文献

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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 ,&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}
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
{"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}
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
{"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}
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
{"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}
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 ,&nbsp;Yuefei Wang ,&nbsp;Zixu Wang ,&nbsp;Jinyan Cao ,&nbsp;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
{"title":"Multiindistinguishability operators","authors":"D. Boixader,&nbsp;J. Recasens","doi":"10.1016/j.ijar.2025.109401","DOIUrl":"10.1016/j.ijar.2025.109401","url":null,"abstract":"<div><div>In this paper (binary) equivalence relations and their fuzzification, indistinguishability operators, are generalized to <em>n</em>-equivalence relations and <em>n</em>-multiindistinguishability operators respectively. Some of the properties of these two last objects are stated as well as their relation with binary ones.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109401"},"PeriodicalIF":3.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529582","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
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
{"title":"DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks","authors":"Loïc Guiziou ,&nbsp;Emmanuel Ramasso ,&nbsp;Sébastien Thibaud ,&nbsp;Sébastien Denneulin","doi":"10.1016/j.ijar.2025.109400","DOIUrl":"10.1016/j.ijar.2025.109400","url":null,"abstract":"<div><div>DEEM (Deep Evidential Encoding of iMages) is a clustering algorithm that combines belief functions with convolutional neural networks in a Siamese-like framework for unsupervised and semi-supervised image clustering. In DEEM, images are mapped to Dempster–Shafer mass functions to quantify uncertainty in cluster membership. Various forms of prior information, including must-link and cannot-link constraints, supervised dissimilarities, and Distance Metric Learning, are incorporated to guide training and improve generalisation. By processing image pairs through shared network weights, DEEM aligns pairwise dissimilarities with the conflict between mass functions, thereby mitigating errors in noisy or incomplete distance matrices. Experiments on MNIST demonstrate that DEEM generalises effectively to unseen data while managing different types of prior knowledge, making it a promising approach for clustering and semi-supervised learning from image data under uncertainty.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109400"},"PeriodicalIF":3.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534895","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
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 ,&nbsp;Andriette Bekker ,&nbsp;Johannes Ferreira ,&nbsp;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
{"title":"Fuzzy time series analysis: Expanding the scope with fuzzy numbers","authors":"Hugo J. Bello ,&nbsp;Manuel Ojeda-Hernández ,&nbsp;Domingo López-Rodríguez ,&nbsp;Carlos Bejines","doi":"10.1016/j.ijar.2025.109387","DOIUrl":"10.1016/j.ijar.2025.109387","url":null,"abstract":"<div><div>This article delves into the process of fuzzifying time series, which entails converting a conventional time series into a time-indexed sequence of fuzzy numbers. The focus lies on the well-established practice of fuzzifying time series when a predefined degree of uncertainty is known, employing fuzzy numbers to quantify volatility or vagueness. To address practical challenges associated with volatility or vagueness quantification, we introduce the concept of informed time series. An algorithm is proposed to derive fuzzy time series, and findings include the examination of structural breaks within the realm of fuzzy time series. Additionally, this article underscores the significance of employing topological tools in the analysis of fuzzy time series, accentuating the role of these tools in extracting insights and unraveling intricate relationships within the data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"180 ","pages":"Article 109387"},"PeriodicalIF":3.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508699","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
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,&nbsp;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
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