International Journal of Approximate Reasoning最新文献_第5页

An approach to calculate conceptual distance across multi-granularity based on three-way partial order structure 基于三向偏序结构的多粒度概念距离计算方法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-22 DOI: 10.1016/j.ijar.2024.109327

Enliang Yan , Pengfei Zhang , Tianyong Hao , Tao Zhang , Jianping Yu , Yuncheng Jiang , Yuan Yang

{"title":"An approach to calculate conceptual distance across multi-granularity based on three-way partial order structure","authors":"Enliang Yan , Pengfei Zhang , Tianyong Hao , Tao Zhang , Jianping Yu , Yuncheng Jiang , Yuan Yang","doi":"10.1016/j.ijar.2024.109327","DOIUrl":"10.1016/j.ijar.2024.109327","url":null,"abstract":"<div><div>The computation of concept distances aids in understanding the interrelations among entities within knowledge graphs and uncovering implicit information. The existing studies predominantly focus on the conceptual distance of specific hierarchical levels without offering a unified framework for comprehensive exploration. To overcome the limitations of unidimensional approaches, this paper proposes a method for calculating concept distances at multiple granularities based on a three-way partial order structure. Specifically: (1) this study introduces a methodology for calculating inter-object similarity based on the three-way attribute partial order structure (APOS); (2) It proposes the application of the similarity matrix to delineate the structure of categories; (3) Based on the similarity matrix describing the three-way APOS of categories, we establish a novel method for calculating inter-category distance. The experiments on eight datasets demonstrate that this approach effectively differentiates various concepts and computes their distances. When applied to classification tasks, it exhibits outstanding performance.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"177 ","pages":"Article 109327"},"PeriodicalIF":3.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723048","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

Robust Bayesian causal estimation for causal inference in medical diagnosis 用于医学诊断因果推理的稳健贝叶斯因果估计

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-22 DOI: 10.1016/j.ijar.2024.109330

Tathagata Basu , Matthias C.M. Troffaes

{"title":"Robust Bayesian causal estimation for causal inference in medical diagnosis","authors":"Tathagata Basu , Matthias C.M. Troffaes","doi":"10.1016/j.ijar.2024.109330","DOIUrl":"10.1016/j.ijar.2024.109330","url":null,"abstract":"<div><div>Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a regressional framework, we assign a treatment and outcome model to estimate the average causal effect. Additionally, for high dimensional regression problems, variable selection methods are also used to find a subset of predictor variables that maximises the predictive performance of the underlying model for better estimation of the causal effect. In this paper, we propose a different approach. We focus on the variable selection aspects of high dimensional causal estimation problem. We suggest a cautious Bayesian group LASSO (least absolute shrinkage and selection operator) framework for variable selection using prior sensitivity analysis. We argue that in some cases, abstaining from selecting (or, rejecting) a predictor is beneficial and we should gather more information to obtain a more decisive result. We also show that for problems with very limited information, expert elicited variable selection can give us a more stable causal effect estimation as it avoids overfitting. Lastly, we carry a comparative study with synthetic dataset and show the applicability of our method in real-life situations.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"177 ","pages":"Article 109330"},"PeriodicalIF":3.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723045","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

Incremental attribute reduction with α,β-level intuitionistic fuzzy sets 用 α、β 级直观模糊集递减属性

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-14 DOI: 10.1016/j.ijar.2024.109326

Pham Viet Anh , Nguyen Ngoc Thuy , Le Hoang Son , Tran Hung Cuong , Nguyen Long Giang

{"title":"Incremental attribute reduction with α,β-level intuitionistic fuzzy sets","authors":"Pham Viet Anh , Nguyen Ngoc Thuy , Le Hoang Son , Tran Hung Cuong , Nguyen Long Giang","doi":"10.1016/j.ijar.2024.109326","DOIUrl":"10.1016/j.ijar.2024.109326","url":null,"abstract":"<div><div>The intuitionistic fuzzy set theory is recognized as an effective approach for attribute reduction in decision information systems containing numerical or continuous data, particularly in cases of noisy data. However, this approach involves complex computations due to the participation of both the membership and non-membership functions, making it less feasible for data tables with a large number of objects. Additionally, in some practical scenarios, dynamic data tables may change in the number of objects, such as the addition or removal of objects. To overcome these challenges, we propose a novel and efficient incremental attribute reduction method based on <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy sets. Specifically, we first utilize the key properties of <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy sets to construct a distance measure between two <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy partitions. This extension of the intuitionistic fuzzy set model helps reduce noise in the data and shrink the computational space. Subsequently, we define a new reduct and design an efficient algorithm to identify an attribute subset in fixed decision tables. For dynamic decision tables, we develop two incremental calculation formulas based on the distance measure between two <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy partitions to improve processing time. Accordingly, some important properties of the distance measures are also clarified. Finally, we design two incremental attribute reduction algorithms that handle the addition and removal of objects. Experimental results have demonstrated that our method is more effective than incremental methods based on fuzzy rough set and intuitionistic fuzzy set approaches in terms of execution time and classification accuracy from the obtained reduct.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109326"},"PeriodicalIF":3.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654677","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 centrality measures in social network analysis: Theory and application in a university department collaboration network 社会网络分析中的模糊中心度量：大学院系协作网络中的理论与应用

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-13 DOI: 10.1016/j.ijar.2024.109319

Annamaria Porreca , Fabrizio Maturo , Viviana Ventre

{"title":"Fuzzy centrality measures in social network analysis: Theory and application in a university department collaboration network","authors":"Annamaria Porreca , Fabrizio Maturo , Viviana Ventre","doi":"10.1016/j.ijar.2024.109319","DOIUrl":"10.1016/j.ijar.2024.109319","url":null,"abstract":"<div><div>The motivation behind this research stems from the inherent complexity and vagueness in human social interactions, which traditional Social Network Analysis (SNA) approaches often fail to capture adequately. Conventional SNA methods typically represent relationships as binary or weighted ties, thereby losing the subtle nuances and inherent uncertainty in real-world social connections. The need to preserve the vagueness of social relations and provide a more accurate representation of these relationships motivates the introduction of a fuzzy-based approach to SNA. This paper proposes a novel framework for Fuzzy Social Network Analysis (FSNA), which extends traditional SNA to accommodate the vagueness of relationships. The proposed method redefines the ties between nodes as fuzzy numbers rather than crisp values and introduces a comprehensive set of fuzzy centrality indices, including fuzzy degree centrality, fuzzy betweenness centrality, and fuzzy closeness centrality, among others. These indices are designed to measure the importance and influence of nodes within a network while preserving the uncertainty in the relationships between them. The applicability of the proposed framework is demonstrated through a case study involving a university department's collaboration network, where relationships between faculty members are analyzed using data collected via a fascinating mouse-tracking technique.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109319"},"PeriodicalIF":3.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654678","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

Goal-hiding information-seeking dialogues: A formal framework 目标隐藏的信息搜索对话：正式框架

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-13 DOI: 10.1016/j.ijar.2024.109325

Andreas Brännström, Virginia Dignum, Juan Carlos Nieves

{"title":"Goal-hiding information-seeking dialogues: A formal framework","authors":"Andreas Brännström, Virginia Dignum, Juan Carlos Nieves","doi":"10.1016/j.ijar.2024.109325","DOIUrl":"10.1016/j.ijar.2024.109325","url":null,"abstract":"<div><div>We consider a type of information-seeking dialogue between a seeker agent and a respondent agent, where the seeker estimates the respondent to not be willing to share a particular set of sought-after information. Hence, the seeker postpones (hides) its goal topic, related to the respondent's sensitive information, until the respondent is perceived as willing to talk about it. In the intermediate process, the seeker opens other topics to steer the dialogue tactfully towards the goal. Such dialogue strategies, which we refer to as goal-hiding strategies, are common in diverse contexts such as criminal interrogations and medical assessments, involving sensitive topics. Conversely, in malicious online interactions like social media extortion, similar strategies might aim to manipulate individuals into revealing information or agreeing to unfavorable terms. This paper proposes a formal dialogue framework for understanding goal-hiding strategies. The dialogue framework uses Quantitative Bipolar Argumentation Frameworks (QBAFs) to assign willingness scores to topics. An initial willingness for each topic is modified by considering how topics promote (support) or demote (attack) other topics. We introduce a method to identify relations among topics by considering a respondent's shared information. Finally, we introduce a gradual semantics to estimate changes in willingness as new topics are opened. Our formal analysis and empirical evaluation show the system's compliance with privacy-preserving safety properties. A formal understanding of goal-hiding strategies opens up a range of practical applications; For instance, a seeker agent may plan with goal-hiding to enhance privacy in human-agent interactions. Similarly, an observer agent (third-party) may be designed to enhance social media security by detecting goal-hiding strategies employed by users' interlocutors.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"177 ","pages":"Article 109325"},"PeriodicalIF":3.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723047","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

Anomaly detection based on improved k-nearest neighbor rough sets 基于改进的 k 近邻粗糙集的异常检测

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-13 DOI: 10.1016/j.ijar.2024.109323

Xiwen Chen , Zhong Yuan , Shan Feng

{"title":"Anomaly detection based on improved k-nearest neighbor rough sets","authors":"Xiwen Chen , Zhong Yuan , Shan Feng","doi":"10.1016/j.ijar.2024.109323","DOIUrl":"10.1016/j.ijar.2024.109323","url":null,"abstract":"<div><div>Neighborhood rough set model is a resultful model for processing incomplete, imprecise, and other uncertain data. It has been used in several fields, such as anomaly detection and data classification. However, most of the current neighborhood rough set models suffer from the issues of unreasonable neighborhood radius determination and poor adaptability. To obtain an adaptive neighborhood radius and make granulation results more reasonable, an improved <em>k</em>-nearest neighbor rough set model is proposed in the paper by introducing <em>k</em>th-distance as the <em>k</em>-nearest neighborhood radius, and an anomaly detection model is constructed. In the method, the <em>k</em>-nearest neighborhood radius is used to calculate the <em>k</em>-nearest neighbor relation firstly. Then, the anomaly degree of granule (GAD) is defined to measure the anomaly degree of <em>k</em>-nearest neighbor granules by combining approximation accuracy with the local density. Furthermore, the GADs of an object's <em>k</em>-nearest neighbor granules generated by different attribute subsets are calculated, and the anomaly score (AS) is constructed. Finally, an anomaly detection algorithm is designed. Some mainstream anomaly detection methods are compared with the proposed method on public datasets. The results indicate that the capability of detecting anomalies of the proposed approach outperforms current detection methods.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109323"},"PeriodicalIF":3.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654676","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

Inner product reduction for fuzzy formal contexts 模糊形式语境的内积还原

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-08 DOI: 10.1016/j.ijar.2024.109324

Qing Wang , Xiuwei Gao

引用次数: 0

Sequential merging and construction of rankings as cognitive logic 作为认知逻辑的序列合并和排名构建

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-08 DOI: 10.1016/j.ijar.2024.109321

Kai Sauerwald , Eda Ismail-Tsaous , Marco Ragni , Gabriele Kern-Isberner , Christoph Beierle

{"title":"Sequential merging and construction of rankings as cognitive logic","authors":"Kai Sauerwald , Eda Ismail-Tsaous , Marco Ragni , Gabriele Kern-Isberner , Christoph Beierle","doi":"10.1016/j.ijar.2024.109321","DOIUrl":"10.1016/j.ijar.2024.109321","url":null,"abstract":"<div><div>We introduce and evaluate a cognitively inspired formal reasoning approach that sequentially applies a combination of a belief merging operator and a ranking construction operator. The approach is inspired by human propositional reasoning, which is understood here as a sequential process in which the agent constructs a new epistemic state in each task step according to newly acquired information. Formally, we model epistemic states by Spohn's ranking functions. The posterior representation of the epistemic state is obtained by merging the prior ranking function and a ranking function constructed from the new piece of information. We denote this setup as the sequential merging approach. The approach abstracts from the concrete merging operation and abstracts from the concrete way of constructing a ranking function according to new information. We formally show that sequential merging is capable of predicting with theoretical maximum achievable accuracy. Various instantiations of our approach are benchmarked on data from a psychological experiment, demonstrating that sequential merging provides formal reasoning methods that are cognitively more adequate than classical logic.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109321"},"PeriodicalIF":3.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654679","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

Multi-sample means comparisons for imprecise interval data 不精确区间数据的多样本均值比较

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-08 DOI: 10.1016/j.ijar.2024.109322

Yan Sun , Zac Rios , Brennan Bean

{"title":"Multi-sample means comparisons for imprecise interval data","authors":"Yan Sun , Zac Rios , Brennan Bean","doi":"10.1016/j.ijar.2024.109322","DOIUrl":"10.1016/j.ijar.2024.109322","url":null,"abstract":"<div><div>In recent years, interval data have become an increasingly popular tool to solving modern data problems. Intervals are now often used for dimensionality reduction, data aggregation, privacy censorship, and quantifying awareness of various uncertainties. Among many statistical methods that are being studied and developed for interval data, significance tests are of particular importance due to their fundamental value both in theory and practice. The difficulty in developing such tests mainly lies in the fact that the concept of normality does not extend naturally to intervals, making the exact tests hard to formulate. As a result, most existing works have relied on bootstrap methods to approximate null distributions. However, this is not always feasible given limited sample sizes or other intrinsic characteristics of the data. In this paper, we propose a novel asymptotic test for comparing multi-sample means with interval data as a generalization of the classic ANOVA. Based on the random sets theory, we construct the test statistic in the form of a ratio of between-group interval variance and within-group interval variance. The limiting null distribution is derived under usual assumptions and mild regularity conditions. Simulation studies with various data configurations validate the asymptotic result, and show promising small sample performances. Finally, a real interval data ANOVA analysis is presented that showcases the applicability of our method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109322"},"PeriodicalIF":3.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654680","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

A robust multi-label feature selection based on label significance and fuzzy entropy 基于标签显著性和模糊熵的鲁棒多标签特征选择

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-07 DOI: 10.1016/j.ijar.2024.109310

Taoli Yang , Changzhong Wang , Yiying Chen , Tingquan Deng

{"title":"A robust multi-label feature selection based on label significance and fuzzy entropy","authors":"Taoli Yang , Changzhong Wang , Yiying Chen , Tingquan Deng","doi":"10.1016/j.ijar.2024.109310","DOIUrl":"10.1016/j.ijar.2024.109310","url":null,"abstract":"<div><div>Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109310"},"PeriodicalIF":3.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654674","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