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

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

Integration of evolutionary prejudices in Dempster-Shafer theory

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-12 DOI: 10.1016/j.ijar.2024.109351

Florence Dupin de Saint-Cyr, Francis Faux

引用次数: 0

Generalized multiview sequential three-way decisions based on local partition order product space

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-09 DOI: 10.1016/j.ijar.2024.109350

Jin Qian , Chuanpeng Zhou , Ying Yu , Mingchen Zheng , Chengxin Hong , Hui Wang

{"title":"Generalized multiview sequential three-way decisions based on local partition order product space","authors":"Jin Qian , Chuanpeng Zhou , Ying Yu , Mingchen Zheng , Chengxin Hong , Hui Wang","doi":"10.1016/j.ijar.2024.109350","DOIUrl":"10.1016/j.ijar.2024.109350","url":null,"abstract":"<div><div>The hierarchical sequential three-way decision model is a method for addressing complex problem-solving. The existing hierarchical sequential three-way decision models mostly employ multi-view and/or multi-level approaches. However, as the number of views increases and the levels deepen, the model becomes too large to solve problems efficiently. In order to solve this problem, this paper proposes a generalized multiview hierarchical sequential three-way decisions based on local partition order product space model. Specifically, we first use a nested partition sequence to represent a view. Next, the linear order relations between levels within the views are split according to the number of levels to obtain local linear order relations. Then, in the multiple views, the local linear order relations between levels close to each other from different views are combined using Cartesian product operations to construct a generalized local partition order product space. Finally, by integrating the hierarchical sequential three-way decisions, the generalized local partition order product space is transformed into a multiview hierarchical sequential three-way decisions model. Experimental results on multiple datasets demonstrate that the proposed multiview hierarchical sequential three-way decision model achieves better performance compared to the existing models.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"178 ","pages":"Article 109350"},"PeriodicalIF":3.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141989","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

Multi-label learning based on neighborhood rough set label-specific features

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-06 DOI: 10.1016/j.ijar.2024.109349

Jiadong Zhang, Jingjing Song, Huige Li, Xun Wang, Xibei Yang

{"title":"Multi-label learning based on neighborhood rough set label-specific features","authors":"Jiadong Zhang, Jingjing Song, Huige Li, Xun Wang, Xibei Yang","doi":"10.1016/j.ijar.2024.109349","DOIUrl":"10.1016/j.ijar.2024.109349","url":null,"abstract":"<div><div>Multi-label learning emerges as a novel paradigm harnessing diverse semantic datasets. Its objective involves eliciting a prognostic framework capable of allocating correlated labels to an unseen instance. Within the multifaceted domain of multi-label learning, the adoption of a label-specific feature methodology is prevalent. This approach entails the induction of a classification model that forecasts the relevance of each class label, utilizing tailored features specific to each label rather than relying on the original features. However, some irrelevant or redundant features will inevitably be generated when constructing features. To address this issue, we extend the current approach and introduce a straightforward yet potent multi-label learning method named NRS-LIFT, i.e., Neighborhood Rough Set Label-specIfic FeaTures. Specifically, a sample selection method is used to reduce the computational complexity, and then a set of tailored features is customized for each label through the neighborhood rough set. Finally, a learning model is induced to predict unseen instances. To fully evaluate the effectiveness of NRS-LIFT, we conduct extensive experiments on 12 multi-label datasets. Compared with mature multi-label learning methods, it is verified that NRS-LIFT has strong performance for multi-label datasets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"178 ","pages":"Article 109349"},"PeriodicalIF":3.2,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141990","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

Simple contrapositive assumption-based argumentation frameworks with preferences: Partial orders and collective attacks

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-03 DOI: 10.1016/j.ijar.2024.109340

Ofer Arieli , Jesse Heyninck

引用次数: 0