Information Sciences最新文献

{"title":"Conjunction subspaces test for conformal and selective classification","authors":"Zengyou He,&nbsp;Zerun Li,&nbsp;Junjie Dong,&nbsp;Xinying Liu,&nbsp;Mudi Jiang,&nbsp;Lianyu Hu","doi":"10.1016/j.ins.2025.122037","DOIUrl":"10.1016/j.ins.2025.122037","url":null,"abstract":"<div><div>In this paper, we present a new classifier, which integrates significance testing results over different random subspaces to yield consensus <em>p</em>-values for quantifying the uncertainty of classification decision. The null hypothesis is that the test sample has no association with the target class on a randomly chosen subspace, and hence the classification problem can be formulated as a problem of testing for the conjunction of hypotheses. The proposed classifier can be easily deployed for the purpose of conformal prediction and selective classification with reject and refine options by simply thresholding the consensus <em>p</em>-values. We provide a theoretical analysis of the generalization error bound for the proposed classifier, along with empirical studies on real datasets to demonstrate its effectiveness.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122037"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534146","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}

{"title":"A novel similarity-based taste features-extracted emotions-aware music recommendation algorithm","authors":"Yu Gao ,&nbsp;Shu-Ping Wan ,&nbsp;Jiu-Ying Dong","doi":"10.1016/j.ins.2025.122001","DOIUrl":"10.1016/j.ins.2025.122001","url":null,"abstract":"<div><div>Human music tastes are subjective and difficult to measure, with existing recommendation algorithms often failing to consider music similarity, taste features, and emotions simultaneously. This paper proposes a novel music recommendation algorithm that integrates music similarity, taste features, and emotions, organized into five modules. Motivated by the probabilistic linguistic term set (PLTS), we establish attribute feature vectors of songs by associating attribute values with corresponding probabilities. Module 1 establishes behavior matrix of users to calculate comprehensive behavioral feeling score for obtaining the original music interests of users. Module 2 designs two improved collaborative filtering algorithms to alleviate data sparsity of intuitionistic fuzzy music taste matrix. Module 3 extracts taste features from user listening behavior and the favorite songs of users. Module 4 integrates subjective and objective music emotions to obtain the comprehensive music emotions of user. Considering the dynamic change in users’ music taste features, we incorporate the latest taste features in Module 5 to reorder the song list obtained by the above four modules. The experiment results verify the effectiveness of this recommendation algorithm. It significantly outperforms three popular music recommendation systems in accuracy, excels in ranking quality, new song accuracy, and richness metrics, marginally surpasses them in listening duration.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122001"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534639","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}

{"title":"A similar environment transfer strategy for dynamic multiobjective optimization","authors":"Junzhong Ji ,&nbsp;Xiaoyu Zhang ,&nbsp;Cuicui Yang ,&nbsp;Xiang Li ,&nbsp;Guangyuan Sui","doi":"10.1016/j.ins.2025.122018","DOIUrl":"10.1016/j.ins.2025.122018","url":null,"abstract":"<div><div>Solving dynamic multiobjective optimization problems (DMOPs) is extremely challenging due to the need to address multiple conflicting objectives that change over time. Transfer prediction-based strategies typically leverage solutions from historical environments to generate an initial population for a new environment. However, these strategies often overlook the similarity between the historical and new environments, which can negatively impact the quality of the initial population. To address this issue, we propose a similar environment transfer strategy. Firstly, we select Pareto-optimal solutions from a randomly generated population in the new environment to form a prior Pareto set (PS). The prior PS is expand by oversampling sparse solutions. Then, we apply the maximum mean discrepancy (MMD) to measure the discrepancy between the prior PS and the PS from each historical environment. The historical environment with the smallest MMD is identified as the similar environment. Finally, we use solutions from this similar environment to establish a kernelized easy transfer learning model, which is employed to predict the quality of random solutions in the new environment. The initial population is formed by combining excellent solutions predicted by the model with the prior PS. Experimental results demonstrate that the proposed strategy significantly outperforms several state-of-the-art strategies.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122018"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512612","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}

{"title":"Edge-enabled personalized fitness recommendations and training guidance for athletes with privacy preservation","authors":"Yuncheng Li ,&nbsp;Cong Li ,&nbsp;Fan Wang","doi":"10.1016/j.ins.2025.122032","DOIUrl":"10.1016/j.ins.2025.122032","url":null,"abstract":"<div><div>In the contemporary era of technological advancements, the demand for personalized fitness solutions tailored to individual athletes' needs and training goals has surged, becoming a pivotal aspect of competitive science and athletic development. This growing trend underscores the need for sophisticated fitness recommendation systems capable of providing customized training regimes. However, such personalization requires the processing of sensitive health and performance data, raising significant privacy concerns among athletes wary of unauthorized data access or misuse. To address these dual challenges, this paper introduces an innovative edge-enabled personalized fitness recommendation system designed specifically for athletes, aiming to harmonize the optimization of personalized training plans with stringent privacy preservation measures. Using the localized processing capabilities of edge computing, our system minimizes latency, enhances real-time data analysis, and significantly reduces the risk of privacy breaches by keeping sensitive data on the athlete's device or in close proximity. We present a comprehensive evaluation of the system's performance through extensive experiments, demonstrating its superior ability to provide personalized fitness recommendations while ensuring robust privacy protection compared to traditional cloud-based solutions. Our findings indicate a promising avenue for adopting edge computing in competitive technology, offering a scalable, efficient, and secure approach to fostering athletic excellence through personalized training.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122032"},"PeriodicalIF":8.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512610","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}

{"title":"Gaussian belief propagation for dynamic obstacle avoidance and formation control in second-order multi-agent systems","authors":"Zexin Huang ,&nbsp;Zhi Liu ,&nbsp;Meijian Tan ,&nbsp;C.L. Philip Chen","doi":"10.1016/j.ins.2025.122022","DOIUrl":"10.1016/j.ins.2025.122022","url":null,"abstract":"<div><div>This paper proposes a control strategy that combines Gaussian Belief Propagation (GBP) with the Artificial Potential Field (APF) method, enabling Multi-Agent Systems (MASs) to achieve global consensus in formation control while flexibly responding to dynamic obstacles within the GBP-based framework. Existing APF-based methods are difficult to cope with fast-moving obstacles exceeding the speed threshold, while the adaptive formation control and stochastic dynamic obstacle avoidance methods proposed in this paper effectively address this challenge. By utilizing the control method proposed in this paper, the MASs are able to accurately predict the future position of such obstacles and pre-plan the obstacle avoidance path. In addition, they are also able to seamlessly return to the desired formation trajectory after effectively solving the collision avoidance challenge, which proves the generalizability of our newly proposed method in various dynamic scenarios. This research offers novel insights and approaches for adaptive control and dynamic obstacle avoidance in MASs.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122022"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512320","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}

{"title":"Ensuring privacy and correlation awareness in multi-dimensional service quality prediction and recommendation for IoT","authors":"Weiyi Zhong ,&nbsp;Wei Fang ,&nbsp;Yifan Zhao ,&nbsp;Sifeng Wang ,&nbsp;Chao Yan ,&nbsp;Rong Jiang ,&nbsp;Maqbool Khan ,&nbsp;Xuan Yang ,&nbsp;Wajid Rafique","doi":"10.1016/j.ins.2025.122017","DOIUrl":"10.1016/j.ins.2025.122017","url":null,"abstract":"<div><div>Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and smart healthcare. However, several challenges arise when fusing multi-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named <em>TLTM</em>) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. Finally, to validate the effectiveness and efficiency of the <em>TLTM</em> method, we have designed extensive experiments on the popular WS-DREAM dataset. The reported experimental results show that our <em>TLTM</em> method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122017"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488958","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}

{"title":"Relaxed naïve Bayesian classifier based on maximum dependent attribute groups","authors":"Gui-Liang Ou ,&nbsp;Yu-Lin He ,&nbsp;Ying-Chao Cheng ,&nbsp;Joshua Zhexue Huang","doi":"10.1016/j.ins.2025.122013","DOIUrl":"10.1016/j.ins.2025.122013","url":null,"abstract":"<div><div>The utilisation of effective dependent attribute groups (DAGs) can benefit the construction of a high-performance naïve Bayesian classifier (NBC), to alleviate the conditional independence assumption of naïve Bayes. An NBC with optimised DAGs retains the simple NBC structure and significantly enhances NBC generalisation performance. However, it is extremely difficult to determine the appropriate DAGs for a given dataset when training an NBC with good generalisation capability. Therefore, this study proposes a relaxed NBC (RNBC) based on the maximum DAGs (MDAGs), that relaxes the attribute independence assumption by constructing an NBC with a series of MDAGs generated from the original condition attribute set. To determine the MDAGs, the RNBC includes an effective objective function to determine the degree of membership of conditional attributes belonging to different DAGs. Unlike the regular computation of class-conditional probability in traditional NBCs with whole condition attributes, the RNBC calculates multiple class-conditional probabilities corresponding to non-overlapping MDAGs and their products are utilised to construct the classification system. Exhaustive experiments were conducted to systematically verify the feasibility, rationality, and effectiveness of RNBC. The results demonstrate that (1) the objective function used to determine the MDAGs is convergent, and that MDAGs can be obtained with low time consumption; (2) the RNBC with MDAGs achieves a lower classification risk than traditional NBCs with the independence assumption; and (3) the RNBC achieves statistically higher training/testing accuracy and probability estimation quality with lower classification risk compared with eight representative Bayesian classifiers spanning 22 benchmark datasets. The best average testing accuracy, probability mean square error, and area under the curve for the RNBC were 0.76, 0.35, and 0.85, respectively. These results systematically confirmed that the proposed RNBC is an efficient NBC variant with high structural stability, strong correlation expression, and good generalisability.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122013"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529498","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}

{"title":"Synchronization time and energy consumption for multiweighted complex networks","authors":"Linlong Xu,&nbsp;Xiwei Liu","doi":"10.1016/j.ins.2025.122019","DOIUrl":"10.1016/j.ins.2025.122019","url":null,"abstract":"<div><div>This paper investigates finite-time, fixed-time, and prescribed-time synchronization of multiweighted complex networks (MCNs) with an emphasis on balancing synchronization time and energy consumption. For finite-time and fixed-time synchronization, the upper bounds for both synchronization time and energy consumption are estimated, with the fixed-time controller offering a settling time (synchronization time) that is independent of the network's initial state. To improve the accuracy of energy estimation, we first use the general <em>p</em>-th power form instead of the commonly used square form. These estimations of synchronization time and energy consumption are then normalized and balanced in a performance evaluation function. Both theoretical and numerical results reveal optimal control gain values, aiding in parameter selection to minimize this performance function. For prescribed-time synchronization, we estimate energy consumption through the exponential integral. To address the challenge of multiple weights, we apply the rearranging variables' order technique (ROT). Using normalized left eigenvectors associated with zero eigenvalues of union matrices after ROT, we construct a Lyapunov function that yields new synchronization criteria for MCNs. Numerical simulations confirm the theoretical findings, demonstrating synchronization under the derived conditions and a clear balance between synchronization time and energy consumption.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122019"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510574","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}

{"title":"GRANA: Graph convolutional network based network representation learning method for attributed network alignment","authors":"Yao Li ,&nbsp;He Cai ,&nbsp;Huilin Liu","doi":"10.1016/j.ins.2025.122014","DOIUrl":"10.1016/j.ins.2025.122014","url":null,"abstract":"<div><div>Social network alignment, which aims at identifying the correspondences of the same users across networks, is the very first step of information process from multiple social networks. Previous efforts on this task are either more inclined to preserve structural consistency or attribute consistency. Therefore, they only achieve good performance on specific alignment tasks or obtain compromised results on all kinds of alignment tasks. To achieve good generalization, in this paper, we propose a novel multi-task learning method to solve different social network alignment tasks, which is named GRANA (Graph convolutional network-based network Representation learning framework for Attributed Network Alignment). Specifically, a new two-layer cross-network convolutional neural network dubbed Cross-GCN is proposed as shared layers of GRANA. And the intra-network and inter-network attribute and structural information are learned respectively with diverse objective functions in the task specific layer of GRANA. To enhance the alignment performance and accelerate the learning process, a weight learning method with a novel weight initialization process is applied. Experimental results on six kinds of datasets show that GRANA outperforms seven state-of-the-art methods by at least 0.002-0.697 in terms of precision@15 value. The ablation studies further support the effectiveness of proposed Cross-GCN and weight initialization process.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122014"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488957","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}

{"title":"Generalized weighted neighborhood rough sets","authors":"Nguyen Ngoc Thuy,&nbsp;Tran Duy Anh,&nbsp;Le Manh Thanh","doi":"10.1016/j.ins.2025.122020","DOIUrl":"10.1016/j.ins.2025.122020","url":null,"abstract":"<div><div>Neighborhood rough sets have been effectively applied to handling numerical data. To more accurately reflect the influence of each condition attribute on the decision attributes, attribute-weighted neighborhood rough sets have been introduced to assign weights to condition attributes when constructing information granules. Additionally, another approach concentrates on weighting objects within granules, aiming to address noisy and unevenly distributed data. However, these approaches only allow the application of weights to either attributes or objects, but not both. Therefore, we propose a novel generalized weighted neighborhood rough set model (GWNRSs), wherein information granules are constructed through a comprehensive evaluation of attribute and object weights. While inheriting the strengths of two previously mentioned approaches, our model also effectively addresses objects in the boundary region, often neglected in traditional models. Theoretically, we present fundamental concepts of GWNRSs and state its essential properties. These properties emphasize that several existing neighborhood rough set models are particular cases of GWNRSs. Next, we develop a robust attribute reduction algorithm based on GWNRSs. Experimentally, we implement the proposed algorithm on various benchmark datasets and compare its performance with other state-of-the-art algorithms. The results in terms of classification accuracy and reduct size demonstrate the superiority of GWNRSs through statistical evaluations.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122020"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512611","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}