International Journal of Approximate Reasoning最新文献_第2页

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

A 4-valued logic for double Stone algebras 双石代数的 4 值逻辑

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-10-24 DOI: 10.1016/j.ijar.2024.109309

Arun Kumar, Neha Gaur, Bisham Dewan

引用次数: 0

Convex expectations for countable-state uncertain processes with càdlàg sample paths 具有 càdlàg 样本路径的可数状态不确定过程的凸期望值

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-10-18 DOI: 10.1016/j.ijar.2024.109308

Alexander Erreygers

引用次数: 0

Approximate inference on optimized quantum Bayesian networks 优化量子贝叶斯网络的近似推理

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2024-10-17 DOI: 10.1016/j.ijar.2024.109307

Walid Fathallah , Nahla Ben Amor , Philippe Leray

{"title":"Approximate inference on optimized quantum Bayesian networks","authors":"Walid Fathallah , Nahla Ben Amor , Philippe Leray","doi":"10.1016/j.ijar.2024.109307","DOIUrl":"10.1016/j.ijar.2024.109307","url":null,"abstract":"<div><div>In recent years, there has been a significant upsurge in the interest surrounding Quantum machine learning, with researchers actively developing methods to leverage the power of quantum technology for solving highly complex problems across various domains. However, implementing gate-based quantum algorithms on noisy intermediate quantum devices (NISQ) presents notable challenges due to limited quantum resources and inherent noise. In this paper, we propose an innovative approach for representing Bayesian networks on quantum circuits, specifically designed to address these challenges and highlight the potential of combining optimized circuits with quantum hybrid algorithms for Bayesian network inference. Our aim is to minimize the required quantum resource needed to implement a Quantum Bayesian network (QBN) and implement quantum approximate inference algorithm on a quantum computer. Through simulations and experiments on IBM Quantum computers, we show that our circuit representation significantly reduces the resource requirements without decreasing the performance of the model. These findings underscore how our approach can better enable practical applications of QBN on currently available quantum hardware.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109307"},"PeriodicalIF":3.2,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531372","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