Information Sciences最新文献_第8页

Dual global information guidance for deep contrastive multi-modal clustering 深度对比多模态聚类的双全局信息引导

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-01 DOI: 10.1016/j.ins.2025.122158

Guoliang Zou, Shizhe Hu, Tongji Chen, Yunpeng Wu, Yangdong Ye

{"title":"Dual global information guidance for deep contrastive multi-modal clustering","authors":"Guoliang Zou, Shizhe Hu, Tongji Chen, Yunpeng Wu, Yangdong Ye","doi":"10.1016/j.ins.2025.122158","DOIUrl":"10.1016/j.ins.2025.122158","url":null,"abstract":"<div><div>Deep contrastive multi-modal clustering (MMC) leverages contrastive learning to capture complementary information across modalities. However, they face two challenges. First, there is a lack of eliminating irrelevant information for downstream tasks in the fused global information. Second, neglecting the guiding role of global information on local information can lead the model to fall into the situation of a locally optimal solution. To address these challenges, we propose a novel dual global information guidance for deep contrastive MMC (DGIG-CMMC) that leverages global information to explore the common and consistent information effectively. Global information contains comprehensive potential information, and DGIG-CMMC implements a global-to-local guidance to solve the problem of local insufficiency. The dual global information guidance includes global feature-guided feature-level (GloG-FeaLv) and global cluster assignments-guided label-level (GloG-LabLv). Specifically, GloG-FeaLv leverages global shared features to guide the private features of each modality, effectively eliminating irrelevant information for downstream tasks. GloG-LabLv employs global cluster assignments to guide and correct mistake partitions at the label-level, then ensuring the consistency and accuracy of clustering. Finally, all modules are optimized jointly in an end-to-end manner to enhance performance. Extensive experimental results confirm that the DGIG-CMMC method improves accuracy by 0.5% to 10.5% compared to state-of-the-art clustering approaches.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122158"},"PeriodicalIF":8.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768665","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

A three-way efficacy evaluation approach with attribute reduction based on weighted temporal fuzzy rough sets 一种基于加权时间模糊粗糙集的属性约简三向效能评价方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-01 DOI: 10.1016/j.ins.2025.122157

Jin Ye , Bingzhen Sun , Xixuan Zhao , Xiaoli Chu

{"title":"A three-way efficacy evaluation approach with attribute reduction based on weighted temporal fuzzy rough sets","authors":"Jin Ye , Bingzhen Sun , Xixuan Zhao , Xiaoli Chu","doi":"10.1016/j.ins.2025.122157","DOIUrl":"10.1016/j.ins.2025.122157","url":null,"abstract":"<div><div>Precise evaluation of clinical efficacy is essential to promote the quality of treatment for patients. Accordingly, some techniques have been used to evaluate or predict the effect of treatment programs, such as statistics, machine learning, and granular computing. In contrast, granular computing can offer flexible, interpretable methods that do not require any prior knowledge to address complex efficacy evaluation problems from a data-driven perspective. Moreover, patients' efficacy information often alternates dynamically between improvement, deterioration, and no change. Granular computing-based methods provide a useful tool for the realization of three-way efficacy classification. Leveraging these advantages, this study attempts to construct a new decision-making approach over the framework of granular computing to deal with a class of efficacy evaluation problems with multi-granularity unbalanced temporal incomplete hybrid decision systems (MGUTIHDSs). To eliminate redundant attributes, we first put forward an attribute reduction method based on weighted temporal fuzzy rough sets. At the same time, several relevant properties are explored. Then, we devise a three-way efficacy evaluation model to objectively complete the personalized evaluation of previous treatment programs. Notwithstanding, it is not feasible to evaluate the efficacy of treatment programs taken at the current time node. To address this issue, a neighborhood-based average similarity prediction method is further developed. Consequently, a three-stage approach including attribute reduction, efficacy evaluation, and efficacy prediction is presented to achieve the efficacy classification of all treatment programs. Finally, the suitability and effectiveness of the approach are demonstrated by a real case study of rheumatoid arthritis. The comparison results indicate that our approach has superior performance, which can provide effective decision support for clinical practice.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122157"},"PeriodicalIF":8.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748158","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

A Bagging algorithm for imprecise classification in cost-sensitive scenarios 成本敏感情况下不精确分类的Bagging算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-01 DOI: 10.1016/j.ins.2025.122151

Serafín Moral-García , Andrés R. Masegosa , Joaquín Abellán

{"title":"A Bagging algorithm for imprecise classification in cost-sensitive scenarios","authors":"Serafín Moral-García , Andrés R. Masegosa , Joaquín Abellán","doi":"10.1016/j.ins.2025.122151","DOIUrl":"10.1016/j.ins.2025.122151","url":null,"abstract":"<div><div>Classic classification aims to predict the value of a variable under study, also called the class variable, based on an attribute set associated with a given item. When the evidence is not strong enough to determine such a value, a set of values of the class variable probably represents a more informative situation. This is called Imprecise Classification. An important aspect of any classification task is the cost that must be assumed in the case of error. Previous research has shown that the fusion/combination of classifiers tends to obtain better predictive results. In Imprecise Classification, there are very few models capable of efficiently fusing information from multiple classifiers. Concerning Imprecise Classification considering error costs, there is no method for this aim in the literature so far. This work presents the first method capable of fusing imprecise classifiers that take into account error costs. To do this, a procedure representing a midpoint between misclassification risk and informative outputs is used as a basis. Experiments highlight that our proposed fusion procedure shows an improvement in the results over those obtained by other methods of the state-of-the-art.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122151"},"PeriodicalIF":8.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760970","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

Irregular mobility: A dynamic alliance formation incentive mechanism under incomplete information 不规则流动：不完全信息下的动态联盟形成激励机制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-01 DOI: 10.1016/j.ins.2025.122155

Zhilin Xu, Hao Sun, Panfei Sun

{"title":"Irregular mobility: A dynamic alliance formation incentive mechanism under incomplete information","authors":"Zhilin Xu, Hao Sun, Panfei Sun","doi":"10.1016/j.ins.2025.122155","DOIUrl":"10.1016/j.ins.2025.122155","url":null,"abstract":"<div><div>The cornerstone of Mobile Crowdsensing is participant mobility, which means different participants will arrive and leave the system separately and their costs also change over time. The uncertainty of participants caused by participants irregular mobility will generate incomplete information, and requesters will be unaware of participants' arrival times, departure times, and cost information. Consequently, the match between requesters and participants by requesters-centric matching algorithm is infeasible because requesters cannot decide on matching strategies because of the lack of knowledge about which participants can be matched. Besides, on account of participant irregular mobility, the match is also unstable due to the irregular changes in participants, requesters may become dissatisfied with the original matching strategies. For incomplete information, a participants-centric matching algorithm where participants have the dominant power is proposed to eliminate the impact of participants' uncertainty. As to the instability of the match, a requesters classification algorithm that could reduce the computational complexity to polynomials is used to update the matching rules. We also promise that with irregular mobility in our algorithm, whichever requester, if not matched in this stage will certainly match participants in the next stage.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122155"},"PeriodicalIF":8.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768664","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

Maximum principle for stochastic partial differential system with fractional Brownian motion 具有分数布朗运动的随机偏微分系统的极大值原理

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-01 DOI: 10.1016/j.ins.2025.122144

Xiaolin Yuan , Guojian Ren , YangQuan Chen , Yongguang Yu

引用次数: 0

Optimized consensus control of multi-manipulator system having actuator fault using reinforcement learning approximation strategy 基于强化学习逼近策略的执行器故障多机械臂系统优化共识控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-31 DOI: 10.1016/j.ins.2025.122141

Yu Cao , Guoxing Wen , Baoshuo Feng , Bin Li

{"title":"Optimized consensus control of multi-manipulator system having actuator fault using reinforcement learning approximation strategy","authors":"Yu Cao , Guoxing Wen , Baoshuo Feng , Bin Li","doi":"10.1016/j.ins.2025.122141","DOIUrl":"10.1016/j.ins.2025.122141","url":null,"abstract":"<div><div>This work is to develop an optimized consensus control of multi-manipulator system by employing reinforcement learning (RL) approximation strategy, while the multi-manipulator system is supposed to have the problem of actuator uncertain fault. The RL approximation strategy aims to avoid directly solving the Hamilton-Jacobi-Bellman (HJB) equation for finding the optimized consensus control because this equation has the strong nonlinearity. Since the manipulator system is modeled by the double-integral dynamic, the RL algorithm needs to simultaneously involve two position and velocity states. Meanwhile, it needs to also consider the compensation of actuator fault consisting of both time-varying efficiency factor and bias signal. By defining the performance function containing the above information, the optimized leader-follower consensus can be competent to work for the multi-manipulator system having actuator fault. Finally, the feasibility and validation of this optimizing consensus method is demonstrated from the two aspects of theory analysis and computer simulation.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122141"},"PeriodicalIF":8.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760971","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

Zilean: A modularized framework for large-scale temporal concept drift type classification Zilean：大规模时间概念漂移类型分类的模块化框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122134

Zhao Deng , QuanXi Feng , Bin Lin , Gary G. Yen

{"title":"Zilean: A modularized framework for large-scale temporal concept drift type classification","authors":"Zhao Deng , QuanXi Feng , Bin Lin , Gary G. Yen","doi":"10.1016/j.ins.2025.122134","DOIUrl":"10.1016/j.ins.2025.122134","url":null,"abstract":"<div><div>In the analysis of time series data, particularly in real-world applications, concept drift classification is crucial for enabling models to adapt in a differentiated manner to future data. To address the challenge of identifying diverse types of drift, we propose Zilean, a novel framework that integrates feature-based and predictor-based techniques while accounting for drift residues and fragmentation during repeated drift detection. The framework incorporates the pre-trained BERT-Base language model into its classifier design, leveraging deep learning for automatic drift classification and eliminating the need for judgment curve analysis. To evaluate its performance, experiments were conducted on a variety of real-world and synthetic datasets, each exhibiting different types of concept drift. The results show that on real-world datasets, our framework achieves a classification accuracy of 91.03%, outperforming XGBoost by 7.94% and surpassing TCN-CNN by 4.28%. Additionally, experiments exploring a frozen parameter strategy and the use of a more lightweight language model, DistilBERT, further enhance accuracy to 96.93% and 97.17%, respectively. These findings underscore the framework's effectiveness in large-scale temporal concept drift classification.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122134"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739395","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

A novel missing value imputation for multi-criteria recommender systems 一种新的多准则推荐系统缺失值估计方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122139

Gökhan Göksel, Ahmet Aydın, Zeynep Batmaz, Cihan Kaleli

{"title":"A novel missing value imputation for multi-criteria recommender systems","authors":"Gökhan Göksel, Ahmet Aydın, Zeynep Batmaz, Cihan Kaleli","doi":"10.1016/j.ins.2025.122139","DOIUrl":"10.1016/j.ins.2025.122139","url":null,"abstract":"<div><div>As the internet continues to expand, users increasingly rely on digital platforms for activities such as movie streaming and travel planning, leading to an overwhelming amount of information. This information overload complicates the process of making efficient and informed decisions. To address this challenge, personalized recommender systems, particularly multi-criteria collaborative filtering (MCCF) models, have been developed to tailor recommendations based on detailed user evaluations across various sub-criteria. However, as the number of criteria in MCCF systems increases, the issue of data sparsity becomes more prominent, with more criteria resulting in more missing evaluations. In this study, we introduce an innovative application of the Bidirectional Encoder Representations from Transformers (BERT) model—well-known for its advances in natural language processing—to impute missing values within MCCF systems. By leveraging contextual insights from user reviews, we hypothesize that BERT can enhance the imputation process, thereby improving coverage and recommendation accuracy in MCCF models. Our experimental results indicate that BERT-based imputation significantly reduces data sparsity and enhances the accuracy and coverage of recommendations. This study underscores BERT's potential in processing linguistic data and highlights its utility in multi-criteria recommender systems. Integrating BERT with MCCF offers a promising advancement in addressing the inherent challenges of personalized recommendation systems.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122139"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739391","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

Optimizing time-dependent multi-agile Earth observation satellite scheduling problem using deep Q-learning and ensemble heuristics 基于深度q学习和集成启发式的时变多敏捷对地观测卫星调度问题优化

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122140

Bohua Li , Ming Chen , Lining Xing , Yingguo Chen , Yingwu Chen

{"title":"Optimizing time-dependent multi-agile Earth observation satellite scheduling problem using deep Q-learning and ensemble heuristics","authors":"Bohua Li , Ming Chen , Lining Xing , Yingguo Chen , Yingwu Chen","doi":"10.1016/j.ins.2025.122140","DOIUrl":"10.1016/j.ins.2025.122140","url":null,"abstract":"<div><div>The Multi-Agile Earth Observation Satellite Scheduling Problem (MAEOSSP) aims to maximize the total observation profits of a multi-satellite system while satisfying temporal and resource constraints. This problem has gained prominence owing to the increasing complexity of task scheduling scenarios and challenges in satellite management and control. MAEOSSP typically consists of two decision-making stages: task allocation and single-satellite scheduling. However, its NP-hard characteristic renders traditional methods susceptible to problem instance variations and computationally intensive. To address these issues, we propose the TAM-ECH, which integrates a Task Allocation Model (TAM) and an Ensemble Construction Heuristic (ECH) to solve MAEOSSP. TAM utilizes a Deep Q-learning Network (DQN) to efficiently allocate tasks to each satellite, while ECH employs ensemble heuristics for fast and stable single-satellite scheduling. Experimental results demonstrate that TAM-ECH outperforms state-of-the-art algorithms in both optimization speed and quality. On average, it achieves a profit rate 6% higher than GRILS and 30% higher than A-ALNS, with reduced solving time. Further experiments validate TAM's efficiency in task allocation and ECH's ability to provide stable, high-quality foundational assistance. Therefore, TAM-ECH provides an efficient solution to MAEOSSP, demonstrating its potential for application in upcoming large constellations and new management modes.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122140"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739394","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

REAPP: A low-cost and accurate reputation evaluation based anonymous privacy preserving scheme in mobile crowdsourcing REAPP：一种低成本、准确的基于声誉评估的移动众包匿名隐私保护方案

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122110

Zeyuan Li , Yinghao Yao , Anfeng Liu , Neal N. Xiong , Shaobo Zhang , Athanasios V. Vasilakos

{"title":"REAPP: A low-cost and accurate reputation evaluation based anonymous privacy preserving scheme in mobile crowdsourcing","authors":"Zeyuan Li , Yinghao Yao , Anfeng Liu , Neal N. Xiong , Shaobo Zhang , Athanasios V. Vasilakos","doi":"10.1016/j.ins.2025.122110","DOIUrl":"10.1016/j.ins.2025.122110","url":null,"abstract":"<div><div>Due to sensitive data, reputation concerns, and uncertain worker behaviors, it is essential for practical Mobile Crowd Sensing (MCS) to preserve privacy and ensure high-quality data when recruiting workers. In this paper, a low-cost and accurate reputation evaluation-based anonymous privacy preserving (REAPP) scheme is proposed to improve data quality and reduce cost for MCS. The important components and innovative aspects of the REAPP scheme are as follows. 1) A low-cost and accurate reputation evaluation (LARE) approach is proposed to select highly trusted workers and obtain high-quality data at a lower cost. The LARE approach utilizes data reported by trusted workers to evaluate the reputation of other workers, and a matrix factorization-based data completion (MFDC) algorithm is adopted to reduce data collection costs. 2) Multilayer linkable spontaneous anonymous group signatures and Paillier encryption are employed in blockchain to conceal workers’ real identities, thereby preserving their reputation and identity privacy. 3) Pedersen commitment and Schnorr signature are adopted to ensure that workers and DR can engage in private transactions and verify their validity, thus protecting the privacy of participants. 4) Proxy re-encryption method is employed to preserve the data of recruited workers from being accessed by unrelated third parties, while reducing costs by not recruiting low-trust workers. Finally, the proposed REAPP scheme is theoretically proven to be correct and effective. Simulations based on real-world datasets illustrate that our REAPP scheme outperforms the state-of-the-art methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122110"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760972","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