International Journal of Approximate Reasoning最新文献_第10页

Exploring the 3-dimensional variability of websites' user-stories using triadic concept analysis 利用三元概念分析探索网站用户故事的三维可变性

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-14 DOI: 10.1016/j.ijar.2024.109248

Alexandre Bazin , Thomas Georges , Marianne Huchard , Pierre Martin , Chouki Tibermacine

{"title":"Exploring the 3-dimensional variability of websites' user-stories using triadic concept analysis","authors":"Alexandre Bazin , Thomas Georges , Marianne Huchard , Pierre Martin , Chouki Tibermacine","doi":"10.1016/j.ijar.2024.109248","DOIUrl":"10.1016/j.ijar.2024.109248","url":null,"abstract":"<div><p>Configurable software systems and families of similar software systems are increasingly being considered by industry to provide software tailored to each customer's needs. Their development requires managing software variability, i.e. commonalities, differences and constraints. A primary step is properly analyzing the variability of software, which can be done at various levels, from specification to deployment. In this paper, we focus on the software variability expressed through user-stories, viz. short formatted sentences indicating which user role can perform which action at the specification level. At this level, variability is usually analyzed in a two dimension view, i.e. software described by features, and considering the roles apart. The novelty of this work is to model the three dimensions of the variability (i.e. software, roles, features) and explore it using Triadic Concept Analysis (TCA), an extension of Formal Concept Analysis. The variability exploration is based on the extraction of 3-dimensional implication rules. The adopted methodology is applied to a case study made of 65 commercial web sites in four domains, i.e. manga, martial arts sports equipment, board games including trading cards, and video-games. This work highlights the diversity of information provided by such methodology to draw directions for the development of a new product or for building software variability models.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109248"},"PeriodicalIF":3.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X2400135X/pdfft?md5=63b0b1042e197f59b2130e6dac07947f&pid=1-s2.0-S0888613X2400135X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708276","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

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

The logic behind desirable sets of things, and its filter representation 理想事物集合背后的逻辑及其过滤表示法

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.2024.109241

Gert de Cooman , Arthur Van Camp , Jasper De Bock

引用次数: 0

Arrow relations in lattices of integer partitions 整数分区网格中的箭头关系

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-27 DOI: 10.1016/j.ijar.2024.109244

Asma'a Almazaydeh , Mike Behrisch , Edith Vargas-García , Andreas Wachtel

引用次数: 0

Aggregation of fuzzy graphs 模糊图的聚合

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-26 DOI: 10.1016/j.ijar.2024.109243

Francisco Javier Talavera , Carlos Bejines , Sergio Ardanza-Trevijano , Jorge Elorza

引用次数: 0

Special issue: Thirteenth international symposium on imprecise probabilities: Theories and applications (ISIPTA’2023) 特刊：第十三届不精确概率国际研讨会：理论与应用（ISIPTA'2023）

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-25 DOI: 10.1016/j.ijar.2024.109246

Ignacio Montes , Enrique Miranda , Barbara Vantaggi

引用次数: 0

A naïve Bayes regularized logistic regression estimator for low-dimensional classification 用于低维分类的天真贝叶斯正则化逻辑回归估计器

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-19 DOI: 10.1016/j.ijar.2024.109239

Yi Tan , Ben Sherwood , Prakash P. Shenoy

{"title":"A naïve Bayes regularized logistic regression estimator for low-dimensional classification","authors":"Yi Tan , Ben Sherwood , Prakash P. Shenoy","doi":"10.1016/j.ijar.2024.109239","DOIUrl":"https://doi.org/10.1016/j.ijar.2024.109239","url":null,"abstract":"<div><p>To reduce the estimator's variance and prevent overfitting, regularization techniques have attracted great interest from the statistics and machine learning communities. Most existing regularized methods rely on the sparsity assumption that a model with fewer parameters predicts better than one with many parameters. This assumption works particularly well in high-dimensional problems. However, the sparsity assumption may not be necessary when the number of predictors is relatively small compared to the number of training instances. This paper argues that shrinking the coefficients towards a low-variance data-driven estimate could be a better regularization strategy for such situations. For low-dimensional classification problems, we propose a naïve Bayes regularized logistic regression (NBRLR) that shrinks the logistic regression coefficients toward the naïve Bayes estimate to provide a reduction in variance. Our approach is primarily motivated by the fact that naïve Bayes is functionally equivalent to logistic regression if naïve Bayes' conditional independence assumption holds. Under standard conditions, we prove the consistency of the NBRLR estimator. Extensive simulation and empirical experimental results show that NBRLR is a competitive alternative to various state-of-the-art classifiers, especially on low-dimensional datasets.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"172 ","pages":"Article 109239"},"PeriodicalIF":3.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484423","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