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

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

Markov conditions and factorization in logical credal networks1 逻辑可信网络中的马尔可夫条件和因式分解1

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-18 DOI: 10.1016/j.ijar.2024.109237

Fabio G. Cozman , Radu Marinescu , Junkyu Lee , Alexander Gray , Ryan Riegel , Debarun Bhattacharjya

引用次数: 0

Frequentist belief update under ambiguous evidence in social networks 社交网络模糊证据下的频繁信念更新

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-17 DOI: 10.1016/j.ijar.2024.109240

Michel Grabisch , M. Alperen Yasar

{"title":"Frequentist belief update under ambiguous evidence in social networks","authors":"Michel Grabisch , M. Alperen Yasar","doi":"10.1016/j.ijar.2024.109240","DOIUrl":"https://doi.org/10.1016/j.ijar.2024.109240","url":null,"abstract":"<div><p>In this paper, we study a frequentist approach to belief updating in the framework of Dempster-Shafer Theory (DST). We propose several mechanisms that allow the gathering of possibly ambiguous pieces of evidence over time to obtain a belief mass assignment. We then use our approach to study the impact of ambiguous evidence on the belief distribution of agents in social networks. We illustrate our approach by taking three representative situations. In the first one, we suppose that there is an unknown state of nature, and agents form belief in the set of possible states. Nature constantly sends a signal which reflects the true state with some probability but which can also be ambiguous. In the second situation, there is no ground truth, and agents are against or in favor of some ethical or societal issues. In the third situation, there is no ground state either, but agents have opinions on left, center, and right political parties. We show that our approach can model various phenomena often observed in social networks, such as polarization or bounded confidence effects.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"172 ","pages":"Article 109240"},"PeriodicalIF":3.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X24001270/pdfft?md5=e461e86b3dd9d7ca88c205f611e3b5d2&pid=1-s2.0-S0888613X24001270-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433806","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

Time series clustering and classification 时间序列聚类和分类特刊

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-06-07 DOI: 10.1016/j.ijar.2024.109238

Pierpaolo D'Urso , Livia De Giovanni , Elizabeth Ann Maharaj

引用次数: 0