International Journal of Approximate Reasoning最新文献

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

Towards machine learning as AGM-style belief change

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.2025.109437

Theofanis Aravanis

{"title":"Towards machine learning as AGM-style belief change","authors":"Theofanis Aravanis","doi":"10.1016/j.ijar.2025.109437","DOIUrl":"10.1016/j.ijar.2025.109437","url":null,"abstract":"<div><div>Artificial Neural Networks (ANNs) are powerful computational models that are able to reproduce complex non-linear processes, and are being widely used in a plethora of contemporary disciplines. In this article, we study the statics and dynamics of a certain class of ANNs, called binary ANNs, from the perspective of belief-change theory. A binary ANN is a feed-forward ANN whose inputs and outputs take binary values, and as such, it is suitable for a wide range of practical applications. For this type of ANNs, we point out that their knowledge (expressed via their input-output relationship) can symbolically be represented in terms of a propositional logic language. Furthermore, in the realm of belief change, we identify the process of changing (revising/contracting) an initial belief set to a modified belief set, as a process of a gradual transition of intermediate belief sets — such a gradualist approach to belief change is more congruent with the behaviors of real-world agents. Along these lines, we provide natural metrics for measuring the distance between these intermediate belief sets, effectively quantifying the disparity in their encoded knowledge. Thereafter, we demonstrate that, similar to belief change, the training process of binary ANNs, through backpropagation, can be emulated via a sequence of successive transitions of belief sets, the distance between which is intuitively related through one of the aforementioned metrics. We also prove that the alluded successive transitions of belief sets can be modeled by means of rational revision and contraction operators, defined within the fundamental belief-change framework of Alchourrón, Gärdenfors and Makinson (AGM). Thus, the process of machine learning (specifically, training binary ANNs) is framed as an operation of AGM-style belief change, offering a modular and logically structured perspective on neural learning.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109437"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768381","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

On optimal scale combinations in generalized multi-scale set-valued ordered information systems

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-27 DOI: 10.1016/j.ijar.2025.109429

Jia-Ru Zhang , Wei-Zhi Wu , Harry F. Lee , Anhui Tan

{"title":"On optimal scale combinations in generalized multi-scale set-valued ordered information systems","authors":"Jia-Ru Zhang , Wei-Zhi Wu , Harry F. Lee , Anhui Tan","doi":"10.1016/j.ijar.2025.109429","DOIUrl":"10.1016/j.ijar.2025.109429","url":null,"abstract":"<div><div>As a computing paradigm inspired by human cognition, granular computing has demonstrated remarkable effectiveness in processing large data sets. Multi-scale rough set analysis, a prominent framework within multi-granular computing, requires optimal scale selection as a critical prerequisite for knowledge extraction from multi-scale data. This study investigates optimal scale selection in generalized multi-scale set-valued ordered information systems (GMSOISs) using Dempster-Shafer evidence theory and information quantification. We first formalize GMSOISs by defining granular information transformations based on inclusion criteria. We then establish dominance relations over object sets induced by attribute subsets under different scale combinations, along with their associated information granules. Building on these constructs, we further derive lower/upper approximations and quantify belief/plausibility degrees of decision dominance classes in generalized multi-scale set-valued ordered decision systems (GMSODSs). Finally, six types of optimal scale combinations are rigorously defined for GMSOISs, consistent GMSODSs, and inconsistent GMSODSs, and their relationships are systematically elucidated. Case studies also validate the proposed theoretical framework with concrete examples.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109429"},"PeriodicalIF":3.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740134","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

Matrix-based approach for knowledge structure construction using variable precision models

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-27 DOI: 10.1016/j.ijar.2025.109427

Chuanyi Huang , Han-liang Huang , Jinjin Li

{"title":"Matrix-based approach for knowledge structure construction using variable precision models","authors":"Chuanyi Huang , Han-liang Huang , Jinjin Li","doi":"10.1016/j.ijar.2025.109427","DOIUrl":"10.1016/j.ijar.2025.109427","url":null,"abstract":"<div><div>Assessment of knowledge acquiring and learning is a complex and multidimensional process that involves the evaluation and measurement of an individual's performance in the process of learning and acquiring knowledge. The concept of fuzzy skill encapsulates an individual's latent cognitive abilities and overall competence. In the disjunctive model, an individual must achieve proficiency in at least one relevant skill to solve an item. In contrast, the conjunctive model requires proficiency in all relevant skills. The disjunctive model's excessive leniency and the conjunctive model's excessive rigor have prompted the development of variable precision <em>α</em>-models to mediate between these extremes. Nonetheless, the variable precision <em>α</em>-model warrants further exploration.</div><div>Consequently, this paper is conducting a comprehensive analysis of the variable precision <em>α</em>-model, presenting three variants, and examining their respective properties. Additionally, no existing algorithm addresses the construction of the knowledge structure within this model. For this purpose, a new matrix operation is defined, and its properties related to fuzzy skill inclusion degree are investigated. The variable precision model is refined for constructing the knowledge structure, and the corresponding algorithm is designed. Moreover, the applicability of the matrix approach in constructing knowledge structures for variable precision models in the context of dynamic items is examined. Finally, a dataset is used to empirically evaluate the feasibility and effectiveness of the proposed algorithm.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109427"},"PeriodicalIF":3.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740135","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 active learning approach to label one million unknown malware variants

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-26 DOI: 10.1016/j.ijar.2025.109426

Ahmed Bensaoud, Jugal Kalita

引用次数: 0

Modeling and updating uncertain evidence within belief function theory

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-25 DOI: 10.1016/j.ijar.2025.109428

Pierre Pomeret-Coquot

{"title":"Modeling and updating uncertain evidence within belief function theory","authors":"Pierre Pomeret-Coquot","doi":"10.1016/j.ijar.2025.109428","DOIUrl":"10.1016/j.ijar.2025.109428","url":null,"abstract":"<div><div>We propose a framework that enhances the expressiveness of the evidential and credal interpretations of Belief Function Theory while remaining within its scope. It allows uncertain evidence to be represented “as is” by associating meaningful intervals of <span><math><mi>N</mi></math></span> or <span><math><mi>R</mi></math></span> to focal elements, providing an intrinsic justification for belief values. This improves the modeling and manipulation of knowledge. From a credal perspective, the framework enables the accurate representation of non-maximal credal sets, when their extrema are belief and plausibility functions.</div><div>We introduce three update operations that extend Dempster's, geometric, and Bayesian conditioning to uncertain evidence. These updates are expressed in terms of transfer of evidence, ensuring linear complexity relative to the number of focal elements. This approach provides clear evidential semantics to Bayesian conditioning, resolves several of its anomalies by making it tractable and commutative, and explains its apparent dilation effect. Most importantly, it accurately yields the updated credal set, rather than merely providing its bounds.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109428"},"PeriodicalIF":3.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726067","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

Superior scoring rules for probabilistic evaluation of single-label multi-class classification tasks

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-24 DOI: 10.1016/j.ijar.2025.109421

Rouhollah Ahmadian , Mehdi Ghatee , Johan Wahlström

{"title":"Superior scoring rules for probabilistic evaluation of single-label multi-class classification tasks","authors":"Rouhollah Ahmadian , Mehdi Ghatee , Johan Wahlström","doi":"10.1016/j.ijar.2025.109421","DOIUrl":"10.1016/j.ijar.2025.109421","url":null,"abstract":"<div><div>This study introduces novel superior scoring rules called Penalized Brier Score (<em>PBS</em>) and Penalized Logarithmic Loss (<em>PLL</em>) to improve model evaluation for probabilistic classification. Traditional scoring rules like Brier Score and Logarithmic Loss sometimes assign better scores to misclassifications in comparison with correct classifications. This discrepancy from the actual preference for rewarding correct classifications can lead to suboptimal model selection. By integrating penalties for misclassifications, <em>PBS</em> and <em>PLL</em> modify traditional proper scoring rules to consistently assign better scores to correct predictions. Formal proofs demonstrate that <em>PBS</em> and <em>PLL</em> satisfy strictly proper scoring rule properties while also preferentially rewarding accurate classifications. Experiments showcase the benefits of using <em>PBS</em> and <em>PLL</em> for model selection, model checkpointing, and early stopping. <em>PBS</em> exhibits a higher negative correlation with the F1 score compared to the Brier Score during training. Thus, <em>PBS</em> more effectively identifies optimal checkpoints and early stopping points, leading to improved F1 scores. Comparative analysis verifies models selected by <em>PBS</em> and <em>PLL</em> achieve superior F1 scores. Therefore, <em>PBS</em> and <em>PLL</em> address the gap between uncertainty quantification and accuracy maximization by encapsulating both proper scoring principles and explicit preference for true classifications. The proposed metrics can enhance model evaluation and selection for reliable probabilistic classification.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109421"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696775","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