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

Towards machine learning as AGM-style belief change 将机器学习作为agm风格的信念改变

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 广义多尺度集值有序信息系统的最优尺度组合

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 基于矩阵的变精度模型知识结构构建方法

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 一种新的主动学习方法来标记一百万个未知的恶意软件变体

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 信念函数理论中不确定证据的建模与更新

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 单标签多类分类任务概率评价的优等评分规则

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

A novel covering rough set model based on granular-ball computing for data with label noise 一种基于颗粒球计算的覆盖粗糙集模型

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-21 DOI: 10.1016/j.ijar.2025.109420

Xiaoli Peng , Yuanlin Gong , Xiang Hou , Zhan Tang , Yabin Shao

{"title":"A novel covering rough set model based on granular-ball computing for data with label noise","authors":"Xiaoli Peng , Yuanlin Gong , Xiang Hou , Zhan Tang , Yabin Shao","doi":"10.1016/j.ijar.2025.109420","DOIUrl":"10.1016/j.ijar.2025.109420","url":null,"abstract":"<div><div>As a novel granular computing model, granular-ball computing (GBC) has a notable advantage of robustness. Inspired by GBC, a granular-ball covering rough set (GBCRS) model whose covering is made up of granular-balls (GBs) is proposed. GBCRS is the first covering rough set that fits the data distribution well. Inheriting the robustness of GBC, GBCRS can work in label noise environments. First, the optimization objective function of GBs in GBCRS is given. In order to ensure the quality of generated GBs, this function is subject to three constraints. Second, the GBCRS model is proposed. The purity threshold is used to relax the related notions so that GBCRS can be used in label noise environments. Subsequently, GBCRS is applied to the covering granular reduction and attribute reduction in label noise environments. In covering granular reduction, we propose an intuitive, understandable and anti-noise GBCRS-based granular reduction (GBCRS-GR) algorithm, which also solves the optimization objective function of GBs. Based on GBCRS-GR, a GBCRS-based attribute reduction (GBCRS-AR) algorithm is proposed with the classification ability of the attribute subset as the evaluation. The experiments on UCI datasets illustrate that proposed algorithm is more robust against label noise than the comparison ones.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109420"},"PeriodicalIF":3.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696859","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

Uncertainty quantification in regression neural networks using evidential likelihood-based inference 基于证据似然推理的回归神经网络中的不确定性量化

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-21 DOI: 10.1016/j.ijar.2025.109423

Thierry Denœux

引用次数: 0

Weightedness measures from inequality systems 权重衡量的是不平等体系

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-20 DOI: 10.1016/j.ijar.2025.109418

Maria Albareda-Sambola , Xavier Molinero , Salvador Roura

{"title":"Weightedness measures from inequality systems","authors":"Maria Albareda-Sambola , Xavier Molinero , Salvador Roura","doi":"10.1016/j.ijar.2025.109418","DOIUrl":"10.1016/j.ijar.2025.109418","url":null,"abstract":"<div><div>A simple game is a cooperative game where some coalitions among players or voters became the (monotonic) set of winning coalitions, and the other ones form the set of losing coalitions. It is well-known that weighted voting games form a strict subclass of simple games, where each player has a voting weight so that a coalition wins if and only if the sum of weights of their members exceeds a given quota, otherwise it loses. This work studies how far away a simple game is for being representable as a weighted voting game, which allows for a more compact representation. There are several <em>measures</em> that determine the <em>weightedness</em> of a simple game, such as the dimension, the trade-robustness, the critical threshold value associated with the <em>α</em>-roughly weightedness property, etc. In this work we propose some new weightedness measures, all based on linear programming. In general terms, for a given simple game, a linear program is used to identify its weightedness: (i) the <em>ϵ</em>-roughly value (<span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>ϵ</mi></mrow></msub></math></span>), (ii) the <span><math><msup><mrow><mi>Z</mi></mrow><mrow><mo>+</mo></mrow></msup></math></span>-roughly value (<span><math><msub><mrow><mi>μ</mi></mrow><mrow><msup><mrow><mi>Z</mi></mrow><mrow><mo>+</mo></mrow></msup></mrow></msub></math></span>), (iii) the Δ-roughly value (<span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>Δ</mi></mrow></msub></math></span>), and (iv) the <em>outlier</em> value (<span><math><msub><mrow><mi>Ψ</mi></mrow><mrow><mi>M</mi></mrow></msub></math></span>). We show a close relation between the known critical threshold value of weightedness and the new measure <span><math><msub><mrow><mi>μ</mi></mrow><mrow><mi>Δ</mi></mrow></msub></math></span>. Finally, we also present an exhaustive comparison of weightedness measures for simple games with up to six players.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109418"},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696774","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

Strong consistency and robustness of fuzzy medoids 模糊介质的强一致性和鲁棒性

IF 3.2 3区计算机科学

International Journal of Approximate Reasoning Pub Date : 2025-03-20 DOI: 10.1016/j.ijar.2025.109425

Beatriz Sinova , Sergio Palacio-Vega , María Ángeles Gil

{"title":"Strong consistency and robustness of fuzzy medoids","authors":"Beatriz Sinova , Sergio Palacio-Vega , María Ángeles Gil","doi":"10.1016/j.ijar.2025.109425","DOIUrl":"10.1016/j.ijar.2025.109425","url":null,"abstract":"<div><div>Central tendency of fuzzy number-valued data can be robustly summarized with different proposals from the literature, namely, fuzzy-valued medians, trimmed means and M-estimators of location. In many applications, fuzzy numbers of a specific shape, such as trapezoidal or triangular, are considered, since the chosen shape scarcely affects the value of these summary measures, whenever the ‘meaning’ is basically preserved. Whereas, irrespective of the considered data shape, M-estimators of location under the conditions of the representer theorem and trimmed means would share the same shape, fuzzy medians do not have to. Fuzzy medians must be frequently approximated through the computation of some of their <em>α</em>-levels, whence methods based on them become more complex computationally. All this might discourage users from choosing these measures to describe central tendency. Fuzzy medoids have been recently introduced as an alternative that keeps both the shape of the data and the idea inspiring fuzzy medians, by focusing on the minimization of the mean distance to sample observations, but constrained to the set of fuzzy-valued data. Consequently, it is guaranteed that they always coincide with a sample observation, like it happens (or can be assumed, by convention, to happen) with the median in real-valued scenarios. This work shows the strong consistency of fuzzy medoids as estimators of the corresponding population median (with respect to the same distance) and their robustness in terms of the finite sample breakdown point. Furthermore, some simulation studies have been developed to compare the finite-sample behaviour of fuzzy medoids and other robust central tendency measures.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109425"},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696860","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