International Journal of Approximate Reasoning最新文献_第9页

On the enumeration of non-dominated matroids with imprecise weights 关于枚举权重不精确的非支配矩阵

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-08-08 DOI: 10.1016/j.ijar.2024.109266

Tom Davot , Tuan-Anh Vu , Sébastien Destercke , David Savourey

引用次数: 0

Pairwise comparison matrices with uniformly ordered efficient vectors 具有均匀有序高效向量的配对比较矩阵

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-08-02 DOI: 10.1016/j.ijar.2024.109265

Susana Furtado , Charles R. Johnson

{"title":"Pairwise comparison matrices with uniformly ordered efficient vectors","authors":"Susana Furtado , Charles R. Johnson","doi":"10.1016/j.ijar.2024.109265","DOIUrl":"10.1016/j.ijar.2024.109265","url":null,"abstract":"<div><p>Our primary interest is understanding reciprocal matrices all of whose efficient vectors are ordinally the same, i.e., there is only one efficient order (we call these matrices uniformly ordered, UO). These are reciprocal matrices for which no efficient vector produces strict order reversals. A reciprocal matrix is called column ordered (CO) if each column is ordinally the same. Efficient vectors for a CO matrix with the same order of the columns always exist. For example, the entry-wise geometric mean of some or all columns of a reciprocal matrix is efficient and, if the matrix is CO, has the same order of the columns. A necessary, but not sufficient, condition for UO is that the matrix be CO and then the only efficient order should be satisfied by the columns (possibly weakly). In the case <span><math><mi>n</mi><mo>=</mo><mn>3</mn></math></span>, CO is necessary and sufficient for UO, but not for <span><math><mi>n</mi><mo>></mo><mn>3</mn></math></span>. We characterize the 4-by-4 UO matrices and identify the three possible alternate orders when the matrix is CO (and give entry-wise conditions for their occurrence). We also describe the simple perturbed consistent matrices that are UO. Some of the technology developed for this purpose is of independent interest.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109265"},"PeriodicalIF":3.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X2400152X/pdfft?md5=46ced9a2ce3c58f5412d1fff892e8440&pid=1-s2.0-S0888613X2400152X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937937","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

The most likely common cause 最常见的原因

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-07-29 DOI: 10.1016/j.ijar.2024.109264

A. Hovhannisyan , A.E. Allahverdyan

{"title":"The most likely common cause","authors":"A. Hovhannisyan , A.E. Allahverdyan","doi":"10.1016/j.ijar.2024.109264","DOIUrl":"10.1016/j.ijar.2024.109264","url":null,"abstract":"<div><p>The common cause principle for two random variables <em>A</em> and <em>B</em> is examined in the case of causal insufficiency, when their common cause <em>C</em> is known to exist, but only the joint probability of <em>A</em> and <em>B</em> is observed. As a result, <em>C</em> cannot be uniquely identified (the latent confounder problem). We show that the generalized maximum likelihood method can be applied to this situation and allows identification of <em>C</em> that is consistent with the common cause principle. It closely relates to the maximum entropy principle. Investigation of the two binary symmetric variables reveals a non-analytic behavior of conditional probabilities reminiscent of a second-order phase transition. This occurs during the transition from correlation to anti-correlation in the observed probability distribution. The relation between the generalized likelihood approach and alternative methods, such as predictive likelihood and minimum common entropy, is discussed. The consideration of the common cause for three observed variables (and one hidden cause) uncovers causal structures that defy representation through directed acyclic graphs with the Markov condition.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"173 ","pages":"Article 109264"},"PeriodicalIF":3.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937938","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

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

Fuzzy object-induced network three-way concept lattice and its attribute reduction 模糊客体诱导网络三向概念网格及其属性还原

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.2024.109251

Miao Liu , Ping Zhu

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