International Journal of Approximate Reasoning最新文献_第8页

Attribute reduction with fuzzy divergence-based weighted neighborhood rough sets 用基于模糊发散的加权邻域粗糙集减少属性

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-26 DOI: 10.1016/j.ijar.2024.109256

Nguyen Ngoc Thuy , Sartra Wongthanavasu

{"title":"Attribute reduction with fuzzy divergence-based weighted neighborhood rough sets","authors":"Nguyen Ngoc Thuy , Sartra Wongthanavasu","doi":"10.1016/j.ijar.2024.109256","DOIUrl":"10.1016/j.ijar.2024.109256","url":null,"abstract":"<div><p>Neighborhood rough sets are well-known as an interesting approach for attribute reduction in numerical/continuous data tables. Nevertheless, in most existing neighborhood rough set models, all attributes are assigned the same weights. This may undermine the capacity to select important attributes, especially for high-dimensional datasets. To establish attribute weights, in this study, we will utilize fuzzy divergence to evaluate the distinction between each attribute with the whole attributes in classifying the objects to the decision classes. Then, we construct a new model of fuzzy divergence-based weighted neighborhood rough sets, as well as propose an efficient attribute reduction algorithm. In our method, reducts are considered under the scenario of the <em>α</em>-certainty region, which is introduced as an extension of the positive region. Several related properties will show that attribute reduction based on the <em>α</em>-certainty region can significantly enhance the ability to identify optimal attributes due to reducing the influence of noisy information. To validate the effectiveness of the proposed algorithm, we conduct experiments on 12 benchmark datasets. The results demonstrate that our algorithm not only significantly reduces the number of attributes compared to the original data but also enhances classification accuracy. In comparison to some other state-of-the-art algorithms, the proposed algorithm also outperforms in terms of classification accuracy for almost all of datasets, while also maintaining a highly competitive reduct size and computation time.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109256"},"PeriodicalIF":3.2,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845491","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

Contribution functions for quantitative bipolar argumentation graphs: A principle-based analysis 量化双极论证图的贡献函数：基于原则的分析

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-23 DOI: 10.1016/j.ijar.2024.109255

Timotheus Kampik , Nico Potyka , Xiang Yin , Kristijonas Čyras , Francesca Toni

引用次数: 0

Beyond conjugacy for chain event graph model selection 链式事件图模型选择的共轭之外

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-20 DOI: 10.1016/j.ijar.2024.109252

Aditi Shenvi , Silvia Liverani

{"title":"Beyond conjugacy for chain event graph model selection","authors":"Aditi Shenvi , Silvia Liverani","doi":"10.1016/j.ijar.2024.109252","DOIUrl":"10.1016/j.ijar.2024.109252","url":null,"abstract":"<div><p>Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian networks, these models can encode context-specific conditional independencies as well as asymmetric developments within the evolution of a process. More recently, new model classes belonging to the chain event graph family have been developed for modelling time-to-event data to study the temporal dynamics of a process. However, existing Bayesian model selection algorithms for chain event graphs and its variants rely on all parameters having conjugate priors. This is unrealistic for many real-world applications. In this paper, we propose a mixture modelling approach to model selection in chain event graphs that does not rely on conjugacy. Moreover, we show that this methodology is more amenable to being robustly scaled than the existing model selection algorithms used for this family. We demonstrate our techniques on simulated datasets.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109252"},"PeriodicalIF":3.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X24001397/pdfft?md5=c8b2ae9a09f0d95817ca9eb40586a0f1&pid=1-s2.0-S0888613X24001397-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951634","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

Difference operators on fuzzy sets 模糊集合上的差分算子

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-19 DOI: 10.1016/j.ijar.2024.109254

Bo Wen Fang

引用次数: 0

Robust weighted fuzzy margin-based feature selection with three-way decision 基于加权模糊边际的稳健特征选择与三向决策

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-18 DOI: 10.1016/j.ijar.2024.109253

Zhenxi Chen , Gong Chen , Can Gao , Jie Zhou , Jiajun Wen

{"title":"Robust weighted fuzzy margin-based feature selection with three-way decision","authors":"Zhenxi Chen , Gong Chen , Can Gao , Jie Zhou , Jiajun Wen","doi":"10.1016/j.ijar.2024.109253","DOIUrl":"10.1016/j.ijar.2024.109253","url":null,"abstract":"<div><p>Feature selection has shown noticeable benefits to the tasks of machine learning and data mining, and an extensive variety of feature selection methods has been proposed to remove redundant and irrelevant features. However, most of the existing methods aim to find a feature subset to perfectly fit data with the minimum empirical risk, thus causing the problems of overfitting and noise sensitivity. In this study, a robust weighted fuzzy margin-based feature selection is proposed for uncertain data with noise. Concretely, a robust weighted fuzzy margin based on fuzzy rough sets is first introduced to evaluate the significance of different features. Then, a gradient ascent algorithm based on the noise filtering strategy and three-way decision is developed to optimize the sample and feature weights to further enlarge the fuzzy margin. Finally, an adaptive feature selection algorithm based on the robust weighted fuzzy margin is presented to generate an optimal feature subset with a large margin. Extensive experiments on the UCI benchmark datasets show that the proposed method could obtain high-quality feature subsets and outperform other representative methods under different noise rates.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109253"},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839433","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

Generalized possibility computation tree logic with frequency and its model checking 带频率的广义可能性计算树逻辑及其模型检查

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-15 DOI: 10.1016/j.ijar.2024.109249

Qing He , Wuniu Liu , Yongming Li

{"title":"Generalized possibility computation tree logic with frequency and its model checking","authors":"Qing He , Wuniu Liu , Yongming Li","doi":"10.1016/j.ijar.2024.109249","DOIUrl":"10.1016/j.ijar.2024.109249","url":null,"abstract":"<div><p>In recent years, there has been significant research in the field of possibilistic temporal logic. However, existing works have not yet addressed the issue of frequency, which is a common form of uncertainty in the real world. This article aims to fill this gap by incorporating frequency information into possibilistic temporal logic and focusing on the model-checking problem of generalized possibility computation tree logic (GPoCTL) with frequency information. Specifically, we introduce generalized possibility computation tree logic with frequency (GPoCTL<sub>F</sub>). Although its syntax can be considered as an extension of frequency constraints of the always operator (□) in GPoCTL, they are fundamentally different in semantics and model-checking methods. To facilitate this extension, useful frequency words such as “always”, “usually”, “often”, “sometimes”, “occasionally”, “rarely”, “hardly ever” and “never” are defined as fuzzy frequency operators in this article. Therefore, this article focuses on investigating the model-checking problem of the frequency-constrained always operator. In addition, we analyze the relationship between some GPoCTL<sub>F</sub> path formulas and GPoCTL path formulas. Then, we provide a model-checking algorithm for GPoCTL<sub>F</sub> and analyze its time complexity. Finally, an example of a social network is used to illustrate the calculation process of the proposed method and its potential applications.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109249"},"PeriodicalIF":3.2,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716870","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

Learning multi-granularity decision implication in correlative data from a logical perspective 从逻辑角度学习关联数据中的多粒度决策含义

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-11 DOI: 10.1016/j.ijar.2024.109250

Shaoxia Zhang , Yanhui Zhai , Deyu Li , Chao Zhang

{"title":"Learning multi-granularity decision implication in correlative data from a logical perspective","authors":"Shaoxia Zhang , Yanhui Zhai , Deyu Li , Chao Zhang","doi":"10.1016/j.ijar.2024.109250","DOIUrl":"10.1016/j.ijar.2024.109250","url":null,"abstract":"<div><p>Formal Concept Analysis (FCA) is a method rooted in order theory, with the aim of analyzing and visually representing concepts. Decision implication serves as a fundamental means of knowledge representation in FCA in the case of decision-making. This paper extends the scope of knowledge discovery within FCA in single domains to the realm of multi-domains, with introducing a framework for knowledge representation and reasoning within correlative data from the perspectives of cross-domain and multi-granularity. Firstly, we delve into the acquisition and modeling of decision knowledge within correlative data, and introduce the concept of multi-granularity decision implication. We then establish multi-granularity decision implication logic to study the completeness, non-redundancy and optimality of multi-granularity decision implications and introduce inference rules with semantical compatibility. Furthermore, we define lattice fusion decision context to seamlessly integrate information within correlative data and construct a multi-granularity decision implication basis (MGDIB) based on lattice fusion decision context. Finally, we conduct an experiment of generating MGDIB based on GroupLens_MovieLens dataset.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109250"},"PeriodicalIF":3.2,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696539","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

Semantic explorations in factorizing Boolean data via formal concepts 通过形式概念对布尔数据进行因式分解的语义探索

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-08 DOI: 10.1016/j.ijar.2024.109247

Radim Belohlavek, Martin Trnecka

引用次数: 0

General inferential limits under differential and Pufferfish privacy 差分和河豚隐私下的一般推理限制

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-02 DOI: 10.1016/j.ijar.2024.109242

James Bailie , Ruobin Gong

{"title":"General inferential limits under differential and Pufferfish privacy","authors":"James Bailie , Ruobin Gong","doi":"10.1016/j.ijar.2024.109242","DOIUrl":"https://doi.org/10.1016/j.ijar.2024.109242","url":null,"abstract":"<div><p>Differential privacy (DP) is a class of mathematical standards for assessing the privacy provided by a data-release mechanism. This work concerns two important flavors of DP that are related yet conceptually distinct: pure <em>ε</em>-differential privacy (<em>ε</em>-DP) and Pufferfish privacy. We restate <em>ε</em>-DP and Pufferfish privacy as Lipschitz continuity conditions and provide their formulations in terms of an object from the imprecise probability literature: the interval of measures. We use these formulations to derive limits on key quantities in frequentist hypothesis testing and in Bayesian inference using data that are sanitised according to either of these two privacy standards. Under very mild conditions, the results in this work are valid for arbitrary parameters, priors and data generating models. These bounds are weaker than those attainable when analysing specific data generating models or data-release mechanisms. However, they provide generally applicable limits on the ability to learn from differentially private data – even when the analyst's knowledge of the model or mechanism is limited. They also shed light on the semantic interpretations of the two DP flavors under examination, a subject of contention in the current literature.<span><sup>1</sup></span></p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"172 ","pages":"Article 109242"},"PeriodicalIF":3.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594254","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

Bimorphisms and attribute implications in heterogeneous formal contexts 异质形式语境中的双态性和属性含义

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-01 DOI: 10.1016/j.ijar.2024.109245

Ľubomír Antoni , Peter Eliaš , Ján Guniš , Dominika Kotlárová , Stanislav Krajči , Ondrej Krídlo , Pavol Sokol , Ľubomír Šnajder

{"title":"Bimorphisms and attribute implications in heterogeneous formal contexts","authors":"Ľubomír Antoni , Peter Eliaš , Ján Guniš , Dominika Kotlárová , Stanislav Krajči , Ondrej Krídlo , Pavol Sokol , Ľubomír Šnajder","doi":"10.1016/j.ijar.2024.109245","DOIUrl":"https://doi.org/10.1016/j.ijar.2024.109245","url":null,"abstract":"<div><p>Formal concept analysis is a powerful mathematical framework based on mathematical logic and lattice theory for analyzing object-attribute relational systems. Over the decades, Formal concept analysis has evolved from its theoretical foundations into a versatile methodology applied across various disciplines. A heterogeneous formal context provides a feasible generalization of a formal context, enabling diverse truth-degrees of objects, attributes, and fuzzy relations. In our paper, we present extended theoretical results on heterogeneous formal contexts, including bimorphisms, Galois connections, and heterogeneous attribute implications. We recall the basic notions and properties of the heterogeneous formal context and its concept lattice. Moreover, we present extended results on bimorphisms and Galois connections in a heterogeneous formal context, including a self-contained proof of the main result. We include examples of introduced notions in heterogeneous formal contexts and two-valued logic. We propose the extension of attribute implications for heterogeneous formal contexts and explore their validity. By embracing heterogeneity in Formal concept analysis, we enrich its extended theoretical foundations and pave the way for innovative applications across diverse domains, including personal data protection and cybersecurity.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"172 ","pages":"Article 109245"},"PeriodicalIF":3.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542117","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