Information Sciences最新文献_第5页

A multidimensional feature grouping sampling algorithm based on dynamic feedback of prior bias 基于先验偏差动态反馈的多维特征分组采样算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122490

Yunwei Zhang , Zongkai Shen , Fang Wang , Jinguo You , Xiaoxia Zhao

{"title":"A multidimensional feature grouping sampling algorithm based on dynamic feedback of prior bias","authors":"Yunwei Zhang , Zongkai Shen , Fang Wang , Jinguo You , Xiaoxia Zhao","doi":"10.1016/j.ins.2025.122490","DOIUrl":"10.1016/j.ins.2025.122490","url":null,"abstract":"<div><div>The rapid development of information technology has led to the generation of massive amounts of large-scale discrete-variable data. However, processing the entire dataset will consume a lot of computing resources and be computationally inefficient. Sampling techniques provide a cost-effective solution to reduce the computational complexity while maintaining the original properties of the data. In pursuit of efficiency and effectiveness, this article proposes a multidimensional feature grouping sampling algorithm based on dynamic feedback of prior bias (MFGS) for sampling discrete-variable data. The basic idea is dynamic feedback iterative sampling. To this end, we established a dynamic feedback correction mechanism based on prior bias, which can accurately locate the sampling feature channel of each iteration, calculate the sampling size of each subgroup, and achieve accurate and targeted cyclic optimization sampling. Meanwhile, MFGS is introduced with the idea of smoothing filtering, which removes redundant samples in the oversampling area and can accurately limit the overall sample size. In addition, we use the multidimensional Manhattan distance to establish a sampling bias evaluation index, which provides a calculation basis for feedback and correction. Finally, we designed three experiments to verify the effectiveness of the feedback correction mechanism and smoothing filtering, and evaluate the sampling accuracy, computational efficiency, and sampling accuracy of the method under additional constraints. The experimental results show that the dynamic feedback correction mechanism and smoothing filter are effective, and MFGS outperforms the compared state-of-the-art methods in terms of sampling accuracy, and its computational efficiency is significantly improved compared with clustering-based sampling methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122490"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Few-shot partial multi-label learning with credible non-candidate label 具有可信非候选标签的少射部分多标签学习

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122485

Meng Wang , Yunfeng Zhao , Zhongmin Yan , Jinglin Zhang , Jun Wang , Guoxian Yu

{"title":"Few-shot partial multi-label learning with credible non-candidate label","authors":"Meng Wang , Yunfeng Zhao , Zhongmin Yan , Jinglin Zhang , Jun Wang , Guoxian Yu","doi":"10.1016/j.ins.2025.122485","DOIUrl":"10.1016/j.ins.2025.122485","url":null,"abstract":"<div><div>Partial multi-label learning (PML) addresses scenarios where each training sample is associated with multiple candidate labels, but only a subset are ground-truth labels. The primary difficulty in PML is to mitigate the negative impact of noisy labels. Most existing PML methods rely on sufficient samples to train a noise-robust multi-label classifier. However, in practical scenarios, such as privacy-sensitive domains or those with limited data, only a few training samples are typically available for the target task. In this paper, we propose an approach called <span>FsPML-CNL</span> (Few-shot Partial Multi-label Learning with Credible Non-candidate Label) to tackle the PML problem with few-shot training samples. Specifically, <span>FsPML-CNL</span> first utilizes the sample features and feature-prototype similarity in the embedding space to disambiguate candidate labels and to obtain label prototypes. Then, the credible non-candidate label is selected based on label correlation and confidence, and its prototype is incorporated into the training samples to generate new data for boosting supervised information. The noise-tolerant multi-label classifier is finally induced with the original and generated samples, along with the confidence-guided loss. Extensive experiments on public datasets demonstrate that <span>FsPML-CNL</span> outperforms competitive baselines across different settings.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122485"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Adaptive structure learning for semi-supervised feature selection with binary single-label learning 基于二元单标签学习的半监督特征选择自适应结构学习

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122498

Huming Liao , Hongmei Chen , Tengyu Yin , Zhong Yuan , Shi-Jinn Horng , Tianrui Li

{"title":"Adaptive structure learning for semi-supervised feature selection with binary single-label learning","authors":"Huming Liao , Hongmei Chen , Tengyu Yin , Zhong Yuan , Shi-Jinn Horng , Tianrui Li","doi":"10.1016/j.ins.2025.122498","DOIUrl":"10.1016/j.ins.2025.122498","url":null,"abstract":"<div><div>Learning pseudo-labels for unlabeled samples provides more helpful information in semi-supervised feature selection (SSFS), and the labels of unlabeled samples are learned as continuous values by most existing SSFS methods. Whereas the given labels of labeled samples are encoded in a one-hot encoding way, the two are not uniform in form and do not provide more explicit supervised information. So, this paper introduces binary single-label learning, which learns unlabeled sample labels into a uniform one-hot encoding form. Furthermore, this paper preserves the data's local and global structure by combining improved Euclidean distance-based adaptive graph learning with sparse representation learning. A novel SSFS model called Adaptive Structure Learning for Semi-supervised Feature Selection with Binary Single-label Learning (ASBLFS) is proposed, and an efficient optimization algorithm is derived. Finally, the following conclusions are observed through extensive experiments with several advanced SSFS models on 15 benchmark datasets: (1) Binary single labels achieve better performance than continuous labels on some datasets, suggesting that binary labels can provide more explicit supervisory information. (2) ASBLFS shows the second-best or best performance on most datasets, demonstrating the superiority of ASBLFS.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122498"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Robust time series forecasting using a novel fuzzy regression approach based on kernel functions 基于核函数的模糊回归鲁棒时间序列预测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122496

Lingtao Kong, Jinyao Wang, Wei Lin

{"title":"Robust time series forecasting using a novel fuzzy regression approach based on kernel functions","authors":"Lingtao Kong, Jinyao Wang, Wei Lin","doi":"10.1016/j.ins.2025.122496","DOIUrl":"10.1016/j.ins.2025.122496","url":null,"abstract":"<div><div>In recent years, the use of fuzzy regression approaches in time series forecasting has increased notably. However, the influence of outliers in time series persists as a significant challenge. In this study, we propose a novel robust fuzzy regression functions approach, which can effectively address the issue of outliers. The proposed method incorporates robust techniques at both the clustering and inference stages. In particular, the fuzzy <em>c</em>-medoids clustering algorithm is employed in the initial stage, while a robust estimator based on kernel functions is utilised in the latter stage. To assess the forecasting performance of the proposed method, two financial time series datasets are considered, including Shanghai Stock Exchange Composite index time series and Taiwan Stock Exchange time series. Furthermore, to evaluate the robustness of the proposed method against outliers, four scenarios of contaminated data are examined. The experimental results demonstrate that the proposed method outperforms several popular methods in the majority of cases for both the original and contaminated datasets.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122496"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Summarizing Boolean and fuzzy tensors with sub-tensors 总结布尔张量和模糊张量的子张量

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122489

Victor Henrique Silva Ribeiro, Loïc Cerf

{"title":"Summarizing Boolean and fuzzy tensors with sub-tensors","authors":"Victor Henrique Silva Ribeiro, Loïc Cerf","doi":"10.1016/j.ins.2025.122489","DOIUrl":"10.1016/j.ins.2025.122489","url":null,"abstract":"<div><div>The disjunctive box cluster model summarizes an <em>n</em>-way Boolean tensor with some of its sub-tensors and their densities, <em>i.e.</em>, the arithmetic means of their values. Mirkin and Kramarenko proposed that easy-to-interpret regression model, for <span><math><mi>n</mi><mo>∈</mo><mo>{</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>}</mo></math></span>, and hill climbing to discover good sub-tensors, according to ordinary least squares. This article generalizes Mirkin and Kramarenko's work: <em>n</em>-way <em>fuzzy</em> tensors are summarized. They encode to what extent <em>n</em>-ary predicates are satisfied. The article also details significant performance improvements to the sequential execution, its parallelization, better starting points for hill climbing, a selection of the discovered sub-tensors, their ranking in order of contribution to the model, and the use of the elbow method to truncate the ordered list. In-depth experiments using synthetic and real-world tensors compare the proposed method, NclusterBox, to Mirkin and Kramarenko's and to the state-of-the-art algorithms for matrix factorization using the max (rather than +) operator and for Boolean tensor factorization. NclusterBox summarizes synthetic and real-world fuzzy tensors more efficiently and, most importantly, more accurately.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122489"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Secure color image encryption algorithm for face recognition using Zaslavsky and Arnold cat maps with binary bit-plane decomposition 安全彩色图像加密算法的人脸识别使用Zaslavsky和阿诺德猫地图与二进制位平面分解

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122502

Wenli Shang , Zhenyu Liu , Shuang Wang , Lei Ding , Xinyi Shang , Zheng Zhou , Lianhai Wang

{"title":"Secure color image encryption algorithm for face recognition using Zaslavsky and Arnold cat maps with binary bit-plane decomposition","authors":"Wenli Shang , Zhenyu Liu , Shuang Wang , Lei Ding , Xinyi Shang , Zheng Zhou , Lianhai Wang","doi":"10.1016/j.ins.2025.122502","DOIUrl":"10.1016/j.ins.2025.122502","url":null,"abstract":"<div><div>During airport security checks, prompt identification and data processing of face images are critical. To address this, a real-time and high-security face image encryption scheme is proposed, which leverages the Haar Cascade Classifier to precisely locate facial regions and encrypts only the identified areas.The encryption algorithm takes the identified facial region and encryption key as inputs. Using the key as a seed, the two-dimensional Zaslavsky chaotic map generates pseudo-random chaotic sequences. A sequence of encryption operations is then performed, including random noise addition, binary bit-plane decomposition, confusion, diffusion, and RGB channel obfuscation. Eventually, the processed image is output as the encrypted face image. For algorithm evaluation, image data are randomly sampled from the IMDB-WIKI dataset. Additionally, a benchmark public dataset is employed to compare the proposed scheme with state-of-the-art encryption methods. Experimental results demonstrate that the scheme exhibits superior encryption security and efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122502"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

MultiPath Island-Based Genetic Algorithm for the K-Most Diverse Near-Shortest Paths 基于多路径岛的k -最多元近最短路径遗传算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-10 DOI: 10.1016/j.ins.2025.122495

Harish Sharma, Edgar Galván, Peter Mooney

{"title":"MultiPath Island-Based Genetic Algorithm for the K-Most Diverse Near-Shortest Paths","authors":"Harish Sharma, Edgar Galván, Peter Mooney","doi":"10.1016/j.ins.2025.122495","DOIUrl":"10.1016/j.ins.2025.122495","url":null,"abstract":"<div><div>Modern routing applications, such as those used for vehicle navigation and emergency response routing, often require access to multiple optimal paths/routes rather than relying on a single optimal solution. However, existing methods typically struggle to balance optimality and diversity within the paths they generate. To address this challenge, we introduce the MultiPath Island-Based Genetic Algorithm (MIBGA) for solving the K-Most Diverse Near-Shortest Paths (KMDNSP) problem, with an emphasis on promoting both path diversity and computation of near-optimal paths. MIBGA is a Parallel Genetic Algorithm (PGA) based on the island model, and our approach incorporates novel migration and selection strategies that preserve diversity across subpopulations of path solutions. Experimental results on large, complex real-world road networks from Arizona, Washington, and Kansas demonstrate MIBGAs superior performance in terms of solution diversity, computational efficiency, and convergence speed compared to other well-established Genetic Algorithm (GA) based approaches. The results of our work further highlight the potential of GAs for addressing complex alternate routing problems in practical real-world settings.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122495"},"PeriodicalIF":8.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Adapting ordered fuzzy numbers to the evaluation of the isolation level of slices 将有序模糊数应用于切片隔离度的评价

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-10 DOI: 10.1016/j.ins.2025.122487

Tomasz W. Nowak, Zbigniew Kotulski

引用次数: 0

An explainable multi-objective genetic programming approach to infer Boolean network 一种可解释的布尔网络多目标遗传规划方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-10 DOI: 10.1016/j.ins.2025.122492

Jinlin Tang, Xiang Liu, Yan Wang, Zhen Quan, Zhicheng Ji

{"title":"An explainable multi-objective genetic programming approach to infer Boolean network","authors":"Jinlin Tang, Xiang Liu, Yan Wang, Zhen Quan, Zhicheng Ji","doi":"10.1016/j.ins.2025.122492","DOIUrl":"10.1016/j.ins.2025.122492","url":null,"abstract":"<div><div>Boolean networks can reflect the causal relationships between different parts of discrete complex systems and predict their state transitions, thereby providing efficient and qualitative insights into such systems. Numerous methods have been investigated to deduce Boolean networks from observed temporal data. However, existing algorithms focus on improving accuracy while neglecting inference explainability. To offer inference explainability without sacrificing accuracy, this paper proposes an explainable multi-objective genetic programming approach to infer large-scale Boolean networks. Unlike existing single-objective algorithms, a new explainable optimization objective is introduced by calculating the average mutual information of the tree nodes. Subsequently, we develop a bio-inspired operation to fully utilize elite solutions and enhance the exploration capability. Additionally, a penalty term for syntax trees is introduced to mitigate overfitting and improve explainability by limiting the tree size. Extensive experiments demonstrate that the proposed approach is interpretable and outperforms current leading algorithms in terms of accurately inferring large-scale Boolean networks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122492"},"PeriodicalIF":8.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

SCS: Subgraph contrastive supervised neural network for link prediction 用于链路预测的子图对比监督神经网络

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-10 DOI: 10.1016/j.ins.2025.122482

Qiming Yang , Wei Wei , Ruizhi Zhang , Xiangnan Feng

{"title":"SCS: Subgraph contrastive supervised neural network for link prediction","authors":"Qiming Yang , Wei Wei , Ruizhi Zhang , Xiangnan Feng","doi":"10.1016/j.ins.2025.122482","DOIUrl":"10.1016/j.ins.2025.122482","url":null,"abstract":"<div><div>Link prediction is a crucial task in network analysis that aims to predict missing or potential links between nodes, with applications spanning social sciences, biology, and computer science. State-of-the-art methods have successfully converted this problem into a binary graph classification task by extracting <em>h</em>-hop subgraph structures. However, this approach blocks information flow outside of <em>h</em>-hop subgraphs and requires additional memory. To address these limitations, we propose an end-to-end link prediction graph neural network incorporating a contrastive learning component. Specifically, we utilize cross-scale contrastive learning to entrench subgraph information by maximizing mutual information between <em>h</em>-hop subgraph information and node representations around the target link. Without explicitly extracting subgraph structures, the proposed method can update node representation with global information while obviating the requirements for additional memory. Extensive experimental results across both plain and attribute graphs demonstrate that our proposed method achieves consistently competitive performance, outperforming other state-of-the-art methods in most cases with satisfying computation cost and fast convergence.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122482"},"PeriodicalIF":8.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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