International Journal of Approximate Reasoning最新文献_第6页

Editorial: Statistical/soft approaches and computing for data analysis and classification

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-09 DOI: 10.1016/j.ijar.2025.109361

Alfonso Gordaliza, Agustín Mayo-Íscar, María Asunción Lubiano, Beatriz Sinova

引用次数: 0

Incremental cognitive learning approach based on concept reduction

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-07 DOI: 10.1016/j.ijar.2024.109359

Taoju Liang , Yidong Lin , Jinjin Li , Guoping Lin , Qijun Wang

{"title":"Incremental cognitive learning approach based on concept reduction","authors":"Taoju Liang , Yidong Lin , Jinjin Li , Guoping Lin , Qijun Wang","doi":"10.1016/j.ijar.2024.109359","DOIUrl":"10.1016/j.ijar.2024.109359","url":null,"abstract":"<div><div>Concept-cognitive learning (CCL) offers an innovative approach to classification, and concept reduction serves as a powerful method for compressing data. Nonetheless, most existing CCLs encounter a significant issue when attempting to downscale the concept space: information loss. This loss leads to cognitive incompleteness and increased complexity. Meanwhile, preserving the native characterization of formal concepts ensures both validity and interpretability for CCL. On the other hand, current incremental CCLs have limited capacity to effectively utilize newly acquired knowledge. In view of these observations, in this article, we propose a novel incremental CCL method based on concept reduction for dynamic classification. To enhance the efficiency of knowledge acquisition, recovery degree is developed to obtain concept reduction from granular concept space. Subsequently, the updating mechanism for concept reduction is explored in dynamic environments. For label recognition, a learning method based on concept reduction is discussed and an incremental learning mechanism for dynamic increased data is further constructed. Empirical studies on fifteen datasets reveal the feasibility and effectiveness of proposed model.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109359"},"PeriodicalIF":3.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093626","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

An extension of overlap functions on convolution lattices

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-07 DOI: 10.1016/j.ijar.2025.109360

Zhi-qiang Liu

{"title":"An extension of overlap functions on convolution lattices","authors":"Zhi-qiang Liu","doi":"10.1016/j.ijar.2025.109360","DOIUrl":"10.1016/j.ijar.2025.109360","url":null,"abstract":"<div><div>Up to now, overlap functions have been expanded into various domains, becoming a significant research topic. The study of extended aggregation operations within a lattice-theoretic structure has garnered momentous interest. In this paper, applying Zadeh's extension principle, we extend overlap functions to convolution lattices. More specifically, (i) we introduce three concepts of extended overlap functions, namely <em>Z</em>-quasi-overlap functions, <em>Z</em>-overlap functions, and <span><math><mi>C</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>L</mi></mrow></msub></math></span>-<em>Z</em>-overlap functions; (ii) we present some useful properties of extended overlap functions on the set of lattice functions; (iii) we apply these properties to show that an extended overlap function is a <span><math><msub><mrow><mn>0</mn></mrow><mrow><mi>δ</mi></mrow></msub></math></span>-<em>Z</em>-overlap function on convolution lattices, providing domain lattice is complete and co-domain lattice a frame. Last but not least, we anticipate that these findings will apply to the general lattice-theoretic framework for type-2 fuzzy systems, and to various areas of soft computing that involve fuzzy logic connectives in type-2 fuzzy sets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109360"},"PeriodicalIF":3.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093625","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

Fuzzy neighborhood-based partial label feature selection via label iterative disambiguation

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-06 DOI: 10.1016/j.ijar.2024.109358

Junqi Li , Wenbin Qian , Wenji Yang , Suxuan Liu , Jintao Huang

{"title":"Fuzzy neighborhood-based partial label feature selection via label iterative disambiguation","authors":"Junqi Li , Wenbin Qian , Wenji Yang , Suxuan Liu , Jintao Huang","doi":"10.1016/j.ijar.2024.109358","DOIUrl":"10.1016/j.ijar.2024.109358","url":null,"abstract":"<div><div>Partial label learning is a specific weakly supervised learning framework in which each training sample is associated with a candidate label set in which the ground-truth label is concealed. Feature selection can remove redundant and irrelevant features to improve the generalization performance of the classification model. However, the impact of ambiguous labels is an essential challenge when adopting feature selection for partial label data. In this paper, a novel two-stage feature selection method is proposed, called fuzzy neighborhood-based partial label feature selection with label iterative disambiguation. In the first stage, the proposed method addresses the issue of noise labels by employing a neighborhood-based iterative strategy to enlarge the gap between ground-truth labels and noisy labels. Subsequently, the labeling confidence induced by label disambiguation is utilized to enhance the robustness of feature selection. In the second stage, feature significance is evaluated using three metrics based on fuzzy neighborhoods. Specifically, fuzzy dependency is obtained using fuzzy relations and labeling confidence. Fuzzy neighborhood entropy-based information gain is proposed as an uncertainty measure. Furthermore, the similarity between samples in the same fuzzy neighborhood is used to estimate neighborhood consistency. The fusion of the above metrics can select more discriminative features for partial label learning. Finally, experimental results on eight controlled UCI datasets and five real-world datasets demonstrate that the proposed method achieves superior performance than other compared methods.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"179 ","pages":"Article 109358"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093624","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

Reduction approaches for fuzzy covering systems

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-06 DOI: 10.1016/j.ijar.2024.109357

Yanbin Feng, Yehai Xie, Guilong Liu

{"title":"Reduction approaches for fuzzy covering systems","authors":"Yanbin Feng, Yehai Xie, Guilong Liu","doi":"10.1016/j.ijar.2024.109357","DOIUrl":"10.1016/j.ijar.2024.109357","url":null,"abstract":"<div><div>A <em>β</em>-covering is an extension of many types of coverings, such as partitions, coverings, and fuzzy coverings. It provides effective approaches to deal with uncertain and fuzzy information. In this paper, we investigate the reduction problem for fuzzy <em>β</em> covering systems and fuzzy <em>β</em> covering decision systems. We propose a reduction algorithm for a fuzzy <em>β</em> covering system such that existing reduction algorithms for a covering system represent a special case. In the existing definition of fuzzy <em>β</em> covering decision systems, the decision attribute must be an equivalence relation; this requirement remains a restriction for applications. To address the issue, we further generalize the definition so that the decision attribute no longer needs to be an equivalence relation. For such fuzzy <em>β</em> covering decision systems, we propose a new discernibility matrix and provide a unified attribute reduction algorithm to identify all reducts. Our work extends the scope of application of attribute reduction. Finally, we use 21 public datasets to verify the effectiveness and feasibility of the proposed algorithms.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"178 ","pages":"Article 109357"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141390","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

Further studies on abstract rough inclusion properties

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-01-02 DOI: 10.1016/j.ijar.2024.109356

Anirban Saha , Jayanta Sen , Mihir Kumar Chakraborty

引用次数: 0

Attribute reduction method based on fuzzy relational equations and inequalities

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-27 DOI: 10.1016/j.ijar.2024.109355

Zofia Matusiewicz, Teresa Mroczek

引用次数: 0

Integral algebra for simulating dynamical systems with interval uncertainties

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-27 DOI: 10.1016/j.ijar.2024.109353

Luc Jaulin

引用次数: 0

Attribute reduction based on weighted neighborhood constrained fuzzy rough sets induced by grouping functions

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-25 DOI: 10.1016/j.ijar.2024.109354

Shan He , Junsheng Qiao , Chengxi Jian

{"title":"Attribute reduction based on weighted neighborhood constrained fuzzy rough sets induced by grouping functions","authors":"Shan He , Junsheng Qiao , Chengxi Jian","doi":"10.1016/j.ijar.2024.109354","DOIUrl":"10.1016/j.ijar.2024.109354","url":null,"abstract":"<div><div>Attribute reduction can extract the most critical attributes from multi-dimensional datasets, this reduces data dimensionality, simplifies data processing and analysis, and the fuzzy rough set (FRS) model-based attribute reduction method is one of the most commonly used attribute reduction methods. In this paper, we construct a new FRS model named G-WNC-FRS for attribute reduction by introducing a new inter-sample distance and two aggregation functions. Specifically, we first introduce the weighted neighborhood constrained distance between samples to make the difference in attributes between different class samples obvious. Then we introduce two not necessarily associative aggregation functions, overlap and grouping functions, to replace the commonly used triangular norms and triangular conorms in FRS model. Finally, we design G-WNC-FRS-based attribute reduction algorithm to select important attributes for classification tasks. Numerical experiments on 11 datasets demonstrate that the attribute reduction algorithm based on G-WNC-FRS has a strong ability to eliminate redundant attributes. Additionally, noise experiments and sensitivity experiments on 4 datasets show that the algorithm has high noise immunity and is able to adapt to different types of datasets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"178 ","pages":"Article 109354"},"PeriodicalIF":3.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141991","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

Measures of association and dependence properties of nested random sets

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-19 DOI: 10.1016/j.ijar.2024.109352

Bernhard Schmelzer

引用次数: 0