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

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

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 three-way decision combining multi-granularity variable precision fuzzy rough set and TOPSIS method 多粒度可变精度模糊粗糙集与 TOPSIS 法相结合的三向决策

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-04 DOI: 10.1016/j.ijar.2024.109318

Chengzhao Jia, Lingqiang Li, Xinru Li

{"title":"A three-way decision combining multi-granularity variable precision fuzzy rough set and TOPSIS method","authors":"Chengzhao Jia, Lingqiang Li, Xinru Li","doi":"10.1016/j.ijar.2024.109318","DOIUrl":"10.1016/j.ijar.2024.109318","url":null,"abstract":"<div><div>This study proposed an innovative fuzzy rough set model to address multi-attribute decision-making problems. Initially, we introduced a novel model of multi-granularity variable precision fuzzy rough sets, which included three foundational models. This model was demonstrated to possess favorable algebraic and topological properties, and particularly noteworthy the comparable property. Subsequently, by integrating the novel model with the TOPSIS method, a novel three-way decision model was proposed. Within this framework, three fundamental models of multi-granularity variable precision fuzzy rough sets were applied in three methods to construct relative loss functions. This resulted in a three-way decision model with three distinct strategies. Finally, we implemented the proposed three-way decision model for risk detection in maternal women. Several experiments and comparisons were conducted to validate the effectiveness, stability, and reliability of our proposed approach. The experimental results indicated that the proposed method accurately classified and ranked maternal women. Overall, our approach offered multiple strategies and fault tolerance and was found to be effective for a large amount of data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109318"},"PeriodicalIF":3.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592975","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

Rough sets, modal logic and approximate reasoning 粗糙集、模态逻辑和近似推理

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-04 DOI: 10.1016/j.ijar.2024.109305

Mihir Kr. Chakraborty , Sandip Majumder , Samarjit Kar

引用次数: 0

Cauchy-Schwarz bounded trade-off weighting for causal inference with small sample sizes 用于小样本因果推断的考奇-施瓦茨有界权衡加权法

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-10-29 DOI: 10.1016/j.ijar.2024.109311

Qin Ma, Shikui Tu, Lei Xu

{"title":"Cauchy-Schwarz bounded trade-off weighting for causal inference with small sample sizes","authors":"Qin Ma, Shikui Tu, Lei Xu","doi":"10.1016/j.ijar.2024.109311","DOIUrl":"10.1016/j.ijar.2024.109311","url":null,"abstract":"<div><div>The difficulty of causal inference for small-sample-size data lies in the issue of inefficiency that the variance of the estimators may be large. Some existing weighting methods adopt the idea of bias-variance trade-off, but they require manual specification of the trade-off parameters. To overcome this drawback, in this article, we propose a Cauchy-Schwarz Bounded Trade-off Weighting (CBTW) method, in which the trade-off parameter is theoretically derived to guarantee a small Mean Square Error (MSE) in estimation. We theoretically prove that optimizing the objective function of CBTW, which is the Cauchy-Schwarz upper-bound of the MSE for causal effect estimators, contributes to minimizing the MSE. Moreover, since the upper-bound consists of the variance and the squared <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-norm of covariate differences, CBTW can not only estimate the causal effects efficiently, but also keep the covariates balanced. Experimental results on both simulation data and real-world data show that the CBTW outperforms most existing methods especially under small sample size scenarios.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109311"},"PeriodicalIF":3.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578198","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