International Journal of Approximate Reasoning最新文献_第4页

Multi-label learning based on neighborhood rough set label-specific features

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-06 DOI: 10.1016/j.ijar.2024.109349

Jiadong Zhang, Jingjing Song, Huige Li, Xun Wang, Xibei Yang

{"title":"Multi-label learning based on neighborhood rough set label-specific features","authors":"Jiadong Zhang, Jingjing Song, Huige Li, Xun Wang, Xibei Yang","doi":"10.1016/j.ijar.2024.109349","DOIUrl":"10.1016/j.ijar.2024.109349","url":null,"abstract":"<div><div>Multi-label learning emerges as a novel paradigm harnessing diverse semantic datasets. Its objective involves eliciting a prognostic framework capable of allocating correlated labels to an unseen instance. Within the multifaceted domain of multi-label learning, the adoption of a label-specific feature methodology is prevalent. This approach entails the induction of a classification model that forecasts the relevance of each class label, utilizing tailored features specific to each label rather than relying on the original features. However, some irrelevant or redundant features will inevitably be generated when constructing features. To address this issue, we extend the current approach and introduce a straightforward yet potent multi-label learning method named NRS-LIFT, i.e., Neighborhood Rough Set Label-specIfic FeaTures. Specifically, a sample selection method is used to reduce the computational complexity, and then a set of tailored features is customized for each label through the neighborhood rough set. Finally, a learning model is induced to predict unseen instances. To fully evaluate the effectiveness of NRS-LIFT, we conduct extensive experiments on 12 multi-label datasets. Compared with mature multi-label learning methods, it is verified that NRS-LIFT has strong performance for multi-label datasets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"178 ","pages":"Article 109349"},"PeriodicalIF":3.2,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141990","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

Simple contrapositive assumption-based argumentation frameworks with preferences: Partial orders and collective attacks

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-12-03 DOI: 10.1016/j.ijar.2024.109340

Ofer Arieli , Jesse Heyninck

引用次数: 0

Extending choice assessments to choice functions: An algorithm for computing the natural extension

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-28 DOI: 10.1016/j.ijar.2024.109331

Arne Decadt, Alexander Erreygers, Jasper De Bock

引用次数: 0

Adding imprecision to hypotheses: A Bayesian framework for testing practical significance in nonparametric settings 增加假设的不精确性：在非参数设置中检验实际意义的贝叶斯框架

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-11-28 DOI: 10.1016/j.ijar.2024.109332

Rodrigo F.L. Lassance , Rafael Izbicki , Rafael B. Stern

{"title":"Adding imprecision to hypotheses: A Bayesian framework for testing practical significance in nonparametric settings","authors":"Rodrigo F.L. Lassance , Rafael Izbicki , Rafael B. Stern","doi":"10.1016/j.ijar.2024.109332","DOIUrl":"10.1016/j.ijar.2024.109332","url":null,"abstract":"<div><div>Instead of testing solely a precise hypothesis, it is often useful to enlarge it with alternatives deemed to differ negligibly from it. For instance, in a bioequivalence study one might test if the concentration of an ingredient is exactly the same in two drugs. In such a context, it might be more relevant to test the enlarged hypothesis that the difference in concentration between them is of no practical significance. While this concept is not alien to Bayesian statistics, applications remain mostly confined to parametric settings and strategies that effectively harness experts' intuitions are often scarce or nonexistent. To resolve both issues, we introduce the Pragmatic Region Oriented Test (<span>PROTEST</span>), an accessible nonparametric testing framework based on distortion models that can seamlessly integrate with Markov Chain Monte Carlo (MCMC) methods and is available as an <span>R</span> package. We develop expanded versions of model adherence, goodness-of-fit, quantile and two-sample tests. To demonstrate how <span>PROTEST</span> operates, we use examples, simulated studies that critically evaluate features of the test and an application on neuron spikes. Furthermore, we address the crucial issue of selecting the threshold—which controls how much a hypothesis is to be expanded—even when intuitions are limited or challenging to quantify.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"178 ","pages":"Article 109332"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757051","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

Existence of optimal strategies in bimatrix game and applications 双矩阵博弈中最优策略的存在及其应用

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.2024.109329

Sana Afreen, Ajay Kumar Bhurjee

{"title":"Existence of optimal strategies in bimatrix game and applications","authors":"Sana Afreen, Ajay Kumar Bhurjee","doi":"10.1016/j.ijar.2024.109329","DOIUrl":"10.1016/j.ijar.2024.109329","url":null,"abstract":"<div><div>This paper delves into interval-valued bimatrix games, where precise payoffs remain elusive, but lower and upper bounds on payoffs can be determined. The study explores several key questions in this context. Firstly, it addresses the issue of the existence of a universally applicable equilibrium across all instances of interval values. The paper establishes a fundamental equivalence by demonstrating that this property hinges on the solvability of a specific system of interval linear inequalities. Secondly, the research endeavors to characterize the comprehensive set of weak and strong equilibrium using a system of interval linear inequalities. The findings in this paper shed light on the complexities and intricacies of interval-valued bimatrix games, offering valuable insights into their equilibrium properties and computational aspects. Through illustrative examples, we underscore the practical utility of these approaches and compare them with previously developed state-of-the-art methods, demonstrating their ability to generate conservative solutions in the face of interval uncertainty. The findings of this research not only offer valuable insights into the equilibrium properties and computational aspects of interval-valued bimatrix games but extend their practical implications. In particular, the paper delves into real-life applications, exemplifying the significance of these findings for crude oil trading decision-making.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"177 ","pages":"Article 109329"},"PeriodicalIF":3.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723046","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

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