International Journal of Approximate Reasoning最新文献

A novel multi-label feature selection method based on conditional entropy and its acceleration mechanism 一种新的基于条件熵的多标签特征选择方法及其加速机制

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-13 DOI: 10.1016/j.ijar.2025.109469

Chengwei Liao, Bin Yang

{"title":"A novel multi-label feature selection method based on conditional entropy and its acceleration mechanism","authors":"Chengwei Liao, Bin Yang","doi":"10.1016/j.ijar.2025.109469","DOIUrl":"10.1016/j.ijar.2025.109469","url":null,"abstract":"<div><div>In multi-label learning, feature selection is a crucial step for enhancing model performance and reducing computational complexity. However, due to the interdependence among labels and the high dimensionality of feature sets, traditional single-label feature selection methods often underperform in multi-label scenarios. Moreover, many existing feature selection methods typically require a comprehensive evaluation of all features and samples in each iteration, resulting in high computational complexity. To address this issue, this paper proposes a feature selection algorithm based on fuzzy conditional entropy within the framework of fuzzy rough set theory. The method gradually identifies optimal features through iterative optimization and systematically filters out features and samples that do not contribute to the current feature subset. Specifically, the filtered features and samples are incorporated into redundant feature and sample sets, thereby dynamically excluding these redundant elements in subsequent iterations and avoiding unnecessary computations. Experiments conducted on 10 multi-label datasets demonstrate that the proposed algorithm outperforms eight other methods in terms of performance.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"185 ","pages":"Article 109469"},"PeriodicalIF":3.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069068","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

Bel coalitional games with application to market games 联合博弈及其在市场博弈中的应用

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-09 DOI: 10.1016/j.ijar.2025.109466

Michel Grabisch , Silvia Lorenzini

引用次数: 0

Graded hexagon of opposition in fuzzy natural logic with new intermediate quantifiers 具有新中间量词的模糊自然逻辑中对立的分级六边形

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-08 DOI: 10.1016/j.ijar.2025.109465

Petra Murinová , Karel Fiala , Stefania Boffa , Vilém Novák

引用次数: 0

General fuzzy difference operator and its application in edge detector 一般模糊差分算子及其在边缘检测中的应用

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-05-02 DOI: 10.1016/j.ijar.2025.109458

Zhi Yu Zhang, Bo Wen Fang

引用次数: 0

About twenty-five naughty entropies in belief function theory: Do they measure informativeness? 信念函数理论中的25个顽皮熵：它们能衡量信息性吗？

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-25 DOI: 10.1016/j.ijar.2025.109454

Radim Jiroušek , Václav Kratochvíl

引用次数: 0

Attention-based credible evidential segmentation network for remote sensing ship segmentation 基于注意力的可信证据分割网络遥感舰船分割

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-24 DOI: 10.1016/j.ijar.2025.109456

SiCong Qu , YiLin Dong , ChangMing Zhu , Lei Cao , KeZhu Zuo

{"title":"Attention-based credible evidential segmentation network for remote sensing ship segmentation","authors":"SiCong Qu , YiLin Dong , ChangMing Zhu , Lei Cao , KeZhu Zuo","doi":"10.1016/j.ijar.2025.109456","DOIUrl":"10.1016/j.ijar.2025.109456","url":null,"abstract":"<div><div>To address the challenges in ship segmentation arising from image quality degradation and edge blurring caused by adverse weather conditions and wave interference, this paper introduces the novel attention-based Credible Evidential Network, CEviCU-Net. Built upon Dempster-Shafer theory and the classical UNet architecture, CEviCU-Net consists of two main components: the feature extraction module and the evidential segmentation module. Specifically, the feature extraction module utilizes the U-shaped encoder-decoder framework. And this framework incorporates the Coordinate Attention Convolutional (CAC) module at the skip connection layers to extract semantic feature vector for each pixel. Subsequently, the evidential segmentation module in the CEviCU-Net employs a distance metric to compute the basic belief assignments (BBAs) between prototypes in the feature space and semantic feature vectors of each pixel. These BBAs are aggregated in the utility layer to facilitate semantic segmentation, accommodating the classification of ambiguous pixels and outliers. Finally, the model is trained end-to-end to jointly optimize all parameters of the proposed CEviCU-Net. Experimental results demonstrate the CEviCU-Net achieves the mIoU scores of 86.89% on the HRSID dataset and 90.43% on the Small ShipInsSeg dataset, representing improvements of 1.97% and 3.34% in average, respectively, compared to baseline models. These findings highlight the effectiveness of our proposed CEviCU-Net in the remote sensing ship segmentation.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"184 ","pages":"Article 109456"},"PeriodicalIF":3.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870597","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

Identifying total causal effects in linear models under partial homoscedasticity 在部分均方差条件下，确定线性模型中的总因果效应

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-17 DOI: 10.1016/j.ijar.2025.109455

David Strieder, Mathias Drton

{"title":"Identifying total causal effects in linear models under partial homoscedasticity","authors":"David Strieder, Mathias Drton","doi":"10.1016/j.ijar.2025.109455","DOIUrl":"10.1016/j.ijar.2025.109455","url":null,"abstract":"<div><div>A fundamental challenge of scientific research is inferring causal relations based on observed data. One commonly used approach involves utilizing structural causal models that postulate noisy functional relations among interacting variables. A directed graph naturally represents these models and reflects the underlying causal structure. However, classical identifiability results suggest that, without conducting additional experiments, this causal graph can only be identified up to a Markov equivalence class of indistinguishable models. Recent research has shown that focusing on linear relations with equal error variances can enable the identification of the causal structure from mere observational data. Nonetheless, practitioners are often primarily interested in the effects of specific interventions, rendering the complete identification of the causal structure unnecessary. In this work, we investigate the extent to which less restrictive assumptions of partial homoscedasticity are sufficient for identifying the causal effects of interest. Furthermore, we construct mathematically rigorous confidence regions for total causal effects under structure uncertainty and explore the performance gain of relying on stricter error assumptions in a simulation study.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109455"},"PeriodicalIF":3.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864662","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 novel multi-step interval prediction model for uncertain data using iterative lower upper bound estimation method 基于迭代下界估计的不确定数据多步区间预测模型

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-11 DOI: 10.1016/j.ijar.2025.109451

Chongyang Xu, Yubin Wang, Shouping Guan

{"title":"A novel multi-step interval prediction model for uncertain data using iterative lower upper bound estimation method","authors":"Chongyang Xu, Yubin Wang, Shouping Guan","doi":"10.1016/j.ijar.2025.109451","DOIUrl":"10.1016/j.ijar.2025.109451","url":null,"abstract":"<div><div>For complex systems, most sensor data are biased, resulting in low accuracy of multi-step prediction results. Based on the lower upper bound estimation (LUBE) method, this paper proposes a multi-step iterative interval prediction model suitable for interval input, which not only maintains the simple structure of the original model, but also realizes single-step and multi-step prediction point (PP) and prediction interval (PI) in different fields. Firstly, an iterative LUBE (ILUBE) network structure, characterized by interval-valued inputs and outputs, is constructed. Secondly, according to this structure, a set of multi-step interval performance indicators are redefined, and a new indicator linear decrease multi-step prediction interval normalized center deviation (LMPINCD) is proposed to measure the prediction horizon, which is added to the multi-step coverage width-based criterion (MCWC). Finally, because MCWC is a non-analytic function, the particle swarm optimization (PSO) meta-heuristic optimization algorithm is used to find the optimal parameters of the ILUBE model. The performance of the model is evaluated using real wind speed and power load datasets. The experimental results show that the model is superior to all other benchmark models in multi-step PI, and also achieves good results in multi-step PP.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109451"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839138","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

Analysis of the syntactic computation of Fagin-Halpern conditioning in possibilistic logic 可能性逻辑中Fagin-Halpern条件的句法计算分析

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-10 DOI: 10.1016/j.ijar.2025.109450

Omar Et-Targuy , Salem Benferhat , Carole Delenne , Ahlame Begdouri

{"title":"Analysis of the syntactic computation of Fagin-Halpern conditioning in possibilistic logic","authors":"Omar Et-Targuy , Salem Benferhat , Carole Delenne , Ahlame Begdouri","doi":"10.1016/j.ijar.2025.109450","DOIUrl":"10.1016/j.ijar.2025.109450","url":null,"abstract":"<div><div>Conditioning is an essential operation in knowledge representation and uncertainty modeling. It enables a priori beliefs to be adjusted in response to new information considered to be fully certain. This work focuses on the computation of Fagin and Halpern (FH-)conditioning in the context where uncertain information is represented by weighted or possibilistic logic belief bases. Weighted belief bases are extensions of classical logic belief bases where a weight or degree of belief is associated with each propositional logic formula. This paper proposes a characterization of the syntactic computation of the revision of weighted belief bases in the light of new information, which is in full agreement with the semantics of the FH-conditioning of possibility distributions. We show that the size of the revised belief base is linear with respect to the size of the initial base and that the computational complexity amounts to performing <span><math><mi>O</mi><mo>(</mo><msub><mrow><mi>log</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></math></span> calls to the propositional logic satisfiability tests, where <em>n</em> is the number of different degrees of certainty used in the initial belief base. The last section of this paper examines both semantically and syntactically FH-conditioning under uncertain information, within the framework of possibility theory.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109450"},"PeriodicalIF":3.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833177","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

An axiom system of the analytic hierarchy process based on the completeness of preferences 基于偏好完备性的层次分析法的一个公理系统

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-10 DOI: 10.1016/j.ijar.2025.109449

Fang Liu , Lin-Tao Dai , Fei-Yang Cheng , Wei-Guo Zhang

{"title":"An axiom system of the analytic hierarchy process based on the completeness of preferences","authors":"Fang Liu , Lin-Tao Dai , Fei-Yang Cheng , Wei-Guo Zhang","doi":"10.1016/j.ijar.2025.109449","DOIUrl":"10.1016/j.ijar.2025.109449","url":null,"abstract":"<div><div>Decision outcomes rely on the preferences of the decision maker (DM) over alternatives. The completeness axiom is a fundamental assumption of preference theory. This paper reports an axiom system of the analytic hierarchy process (AHP) under the theory of preferences. First, the existing axiomatic foundations of AHP are analyzed. It is found that the Saaty's one is limited by strict mathematical intuition for the reciprocal property of comparison ratios. The uncertainty-induced one could lead to the confusion of preferences owing to the breaking of reciprocal property. Second, the constrained relation of comparison ratios is constructed according to the completeness of preferences, which is used to form a novel axiom system of AHP. Third, the concepts of ordinal consistency and acceptable ordinal consistency are developed for pairwise comparison matrices (PCMs). The index of measuring ordinal consistency is constructed using Spearman rank correlation coefficient to overcome the shortcoming in the existing one. The prioritization method is discussed to reveal that the geometric mean method is suitable for PCMs with ordinal consistency. It is revealed that the developed axiomatic foundation of AHP reasonably embeds the preference information of DMs. The results help to identify how to flexibly apply AHP in practice by considering the complete preferences of DMs under uncertainty.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109449"},"PeriodicalIF":3.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833176","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