International Journal of Approximate Reasoning最新文献

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

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

Multi-label feature selection based on adaptive label enhancement and class-imbalance-aware fuzzy information entropy 基于自适应标签增强和类不平衡感知模糊信息熵的多标签特征选择

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-06 DOI: 10.1016/j.ijar.2024.109320

Qiong Liu , Mingjie Cai , Qingguo Li , Chaoqun Huang

{"title":"Multi-label feature selection based on adaptive label enhancement and class-imbalance-aware fuzzy information entropy","authors":"Qiong Liu , Mingjie Cai , Qingguo Li , Chaoqun Huang","doi":"10.1016/j.ijar.2024.109320","DOIUrl":"10.1016/j.ijar.2024.109320","url":null,"abstract":"<div><div>Multi-label feature selection can select representative features to reduce the dimension of data. Since existing multi-label feature selection methods usually suppose that the significance of all labels is consistent, the relationships between samples in the entire label space are generated straightforwardly such that the shape of label distribution and the property of class-imbalance are ignored. To address these issues, we propose a novel multi-label feature selection approach. Based on non-negative matrix factorization (NMF), the similarities between the logical label and label distribution are constrained, which ensures that the shape of label distribution does not deviate from the underlying actual shape to some extent. Further, the relationships between samples in label space and feature space are restricted by graph embedding. Finally, we leverage the properties of label distribution and class-imbalance to generate the relationships between samples in label space and propose a multi-label feature selection approach based on fuzzy information entropy. Eight state-of-the-art methods are compared with the proposed method to validate the effectiveness of our method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109320"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654296","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 Bayesian network interpretation of the Cox's proportional hazard model Cox比例风险模型的贝叶斯网络解释

IF 3.9 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2018-12-01 DOI: 10.1016/j.ijar.2018.09.007

Jidapa Kraisangka , Marek J. Druzdzel

{"title":"A Bayesian network interpretation of the Cox's proportional hazard model","authors":"Jidapa Kraisangka , Marek J. Druzdzel","doi":"10.1016/j.ijar.2018.09.007","DOIUrl":"10.1016/j.ijar.2018.09.007","url":null,"abstract":"<div><p>Cox's proportional hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent a relationship between a collection of risks and their common effect, Bayesian networks have become an attractive alternative with an increased modeling power and far broader applications. Our paper focuses on a Bayesian network interpretation of the CPH model (BN-Cox). We provide a method of encoding knowledge from existing CPH models in the process of knowledge engineering for Bayesian networks. This is important because in practice we often have CPH models available in the literature and no access to the original data from which they have been derived.</p><p>We compare the accuracy of the resulting BN-Cox model to the original CPH model, Kaplan–Meier estimate, and Bayesian networks learned from data, including Naive Bayes, Tree Augmented Naive Bayes, Noisy-Max, and parameter learning by means of the EM algorithm. BN-Cox model came out as the most accurate of all BN approaches and very close to the original CPH model.</p><p>We study two approaches for simplifying the BN-Cox model for the sake of representational and computational efficiency: (1) parent divorcing and (2) removing less important risk factors. We show that removing less important risk factors leads to smaller loss of accuracy.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"103 ","pages":"Pages 195-211"},"PeriodicalIF":3.9,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ijar.2018.09.007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37279021","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: 9

A constraint optimization approach to causal discovery from subsampled time series data 从次采样时间序列数据中发现因果关系的约束优化方法

IF 3.9 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2017-11-01 DOI: 10.1016/j.ijar.2017.07.009

Antti Hyttinen , Sergey Plis , Matti Järvisalo , Frederick Eberhardt , David Danks

{"title":"A constraint optimization approach to causal discovery from subsampled time series data","authors":"Antti Hyttinen , Sergey Plis , Matti Järvisalo , Frederick Eberhardt , David Danks","doi":"10.1016/j.ijar.2017.07.009","DOIUrl":"10.1016/j.ijar.2017.07.009","url":null,"abstract":"<div><p>We consider causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. We then apply the method to real-world data, investigate the robustness and scalability of our method, consider further approaches to reduce underdetermination in the output, and perform an extensive comparison between different solvers on this inference problem. Overall, these advances build towards a full understanding of non-parametric estimation of system timescale causal structures from subsampled time series data.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"90 ","pages":"Pages 208-225"},"PeriodicalIF":3.9,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ijar.2017.07.009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36093009","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: 16