International Journal of Approximate Reasoning最新文献

Attention-based credible evidential segmentation network for remote sensing ship segmentation

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

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

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

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

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

Box atlas: An interval version of atlas

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-04 DOI: 10.1016/j.ijar.2025.109441

Arthur Ignazi, Remy Guyonneau, Sébastien Lagrange, Sébastien Lahaye

引用次数: 0

DFF-Net: Dynamic feature fusion network for time series prediction

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-02 DOI: 10.1016/j.ijar.2025.109436

Bin Xiao , Zheng Chen , Yanxue Wu , Min Wang , Shengtong Hu , Xingpeng Zhang

{"title":"DFF-Net: Dynamic feature fusion network for time series prediction","authors":"Bin Xiao , Zheng Chen , Yanxue Wu , Min Wang , Shengtong Hu , Xingpeng Zhang","doi":"10.1016/j.ijar.2025.109436","DOIUrl":"10.1016/j.ijar.2025.109436","url":null,"abstract":"<div><div>Time series forecasting predicts future values based on historical observations within a sequential dataset. However, popular attention mechanisms exhibit high computational complexity when it comes to capturing channel correlations. Additionally, multi-scale feature fusion methods often generate information redundancy when processing diverse features, which can result in unstable model learning. In this paper, we propose a Dynamic Feature Fusion Network (DFF-Net) to address the aforementioned challenges. The network consists of two key modules: the Stochastic Feature Aggregator (SFA) and the Dimensional Mixer (DMix). First, the SFA module extracts core feature representations by utilizing random training sampling and weighted averaging during the inference process. These core features are then integrated with individual feature representations. Second, the DMix module utilizes scalable dimensional transformations to achieve feature compression and reconstruction. The compressed features and the reconstructed features are then concatenated to enhance data representations. Experimental results demonstrate that DFF-Net outperforms seven state-of-the-art methods in both prediction accuracy and computational efficiency across multiple benchmark time series datasets.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109436"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759329","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 equivalent characterization for admissible orders on n-dimensional intervals generated by matrices

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-02 DOI: 10.1016/j.ijar.2025.109438

Wei Zhang

引用次数: 0

Bipolar decomposition integrals

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-02 DOI: 10.1016/j.ijar.2025.109439

Jabbar Abbas , Radko Mesiar , Radomír Halaš

{"title":"Bipolar decomposition integrals","authors":"Jabbar Abbas , Radko Mesiar , Radomír Halaš","doi":"10.1016/j.ijar.2025.109439","DOIUrl":"10.1016/j.ijar.2025.109439","url":null,"abstract":"<div><div>The idea of decomposition integral, inspired by the concept of Lebesgue integral, is a common framework for unifying many nonlinear integrals, such as the Choquet, the Shilkret, the PAN, and the concave integrals. This framework concerns aggregation on a unipolar scale, and depends on the distinguished decomposition system under some constraints on the sets being considered for each related integral. The aim of this paper is to provide a general framework to deal with integrals concerning aggregation on unipolar and bipolar scales. To achieve this aim, we propose in this paper an extension of the idea of decomposition integral of the integrated function to be suitable for bipolar scales depending on the distinguished bipolar decomposition system under some constraints on the bipolar collections being considered for each related bipolar fuzzy integral. Then, we introduce some properties of bipolar decomposition integrals, including those establishing that our approach covers the Cumulative Prospect Theory (CPT) model and the integrals with respect to bipolar capacities. Finally, we conclude with certain directions on some additional findings related to the research.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109439"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768384","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

Prospect utility with hyperbolic tangent function

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-04-02 DOI: 10.1016/j.ijar.2025.109440

Patrick Adjei, Santiago Gomez-Rosero, Miriam A.M. Capretz

{"title":"Prospect utility with hyperbolic tangent function","authors":"Patrick Adjei, Santiago Gomez-Rosero, Miriam A.M. Capretz","doi":"10.1016/j.ijar.2025.109440","DOIUrl":"10.1016/j.ijar.2025.109440","url":null,"abstract":"<div><div>One branch of safety in reinforcement learning is through integrating risk sensitivity within the Markov Decision Process framework. The objective is to mitigate low-probability events that could lead to severe negative outcomes. Eliminating such risky events is usually done by incorporating a utility function on the expected return; therefore, reshaping the reward structures according to the risk levels associated with different outcomes. The temporal difference learning algorithm can be modified with a utility to capture risk. Notably, such utility functions are either convex or concave depending on the desired risk behavior. Given the outcome space and depending on the risk-sensitivity mode, concave utilities may promote risk-averse behavior and convex utilities may encourage risk-seeking strategies. Such function structure is demonstrated in Prospect Theory, and this motivates a novel formulation using the hyperbolic tangent function called PTanh. Using PTanh, experiments are performed to assess the effect of the diminishing marginal property on the risk-averse policies. It is concluded that there is a correlation between the marginal and selecting the risk-averse parameters. The marginals influence the effectiveness of the averse policies. When the marginals are considered, PTanh can demonstrate better results in terms of a ratio of average reward per prohibited state rate. Furthermore, using empirical evidence, the policy experiments shown with PTanh generalize to other utilities of the Prospect Shape.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109440"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768383","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