Information Sciences最新文献

{"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}

{"title":"Compact agent neighborhood search for the SCSGA-MF-TS: SCSGA with multi-dimensional features prioritizing task satisfaction","authors":"Tuhin Kumar Biswas ,&nbsp;Avisek Gupta ,&nbsp;Narayan Changder ,&nbsp;Swagatam Das ,&nbsp;Redha Taguelmimt ,&nbsp;Samir Aknine ,&nbsp;Animesh Dutta","doi":"10.1016/j.ins.2025.122021","DOIUrl":"10.1016/j.ins.2025.122021","url":null,"abstract":"<div><div>A variant of the Simultaneous Coalition Structure Generation and Assignment (SCSGA) problem considering Multi-dimensional Features (SCSGA-MF) aims to form coalitions of multi-dimensional agents to satisfy the requirements of multi-dimensional tasks. Considering multiple dimensions for agents and tasks makes identifying optimal solutions challenging. However this problem setup is more human-interpretable, as each task feature can be viewed as a requirement to be met by the agent features in a coalition. Previous research on SCSGA-MF focused on minimizing the value of the coalition structure, while maximizing task satisfaction has yet to be explored. Here we propose the SCSGA-MF prioritizing Task Satisfaction (SCSGA-MF-TS), which aims to minimize the coalition structure value while maximizing the number of satisfied tasks. For SCSGA-MF-TS, we propose a Compact Agent Neighborhood (CAN) search consisting of two phases. The first phase generates an initial coalition structure by assigning agents to the nearest yet-unsatisfied tasks. The second phase refines the coalition structure by assigning agents to coalitions with the most compact local neighborhood around its task, while not decreasing the number of satisfied tasks. Our empirical studies show that the CAN search satisfies significantly more tasks compared to the state-of-the-arts. For a relaxed SCSGA-MF-TS problem, a greedy heuristic is recommended.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122021"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510572","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 rapid cross-validation computing for three-way decisions in imbalanced data","authors":"Jianfeng Xu ,&nbsp;Xing Liu ,&nbsp;Zhenzhen Gu ,&nbsp;Guohui Xiao","doi":"10.1016/j.ins.2025.122016","DOIUrl":"10.1016/j.ins.2025.122016","url":null,"abstract":"<div><div>Three-way decisions (TWDs) developed from rough set theory play a crucial role in decision-making and have been widely applied across various scenarios. However, the prevalence of imbalanced data in real-world applications poses significant challenges to TWDs. Traditional TWD approaches often overlook the impact of imbalanced data, leading to suboptimal performance when applied to datasets with non-uniform class distributions. Stratified <em>K</em>-fold cross-validation is a popular technique for evaluating models on imbalanced datasets. In this paper, we introduce stratified <em>K</em>-fold based cross-validation to TWDs, so as to enhance the models' reliability and accuracy. Nonetheless, implementing stratified <em>K</em>-fold cross-validation to TWDs requires training the models <em>K</em>-times, leading to high computational complexity. By analyzing the data models for stratified <em>K</em>-fold cross-validation, we provide an approach of carrying out rapid validation in TWDs via reducing computation as much as possible, so as to improve the overall performance. Theoretical results can guarantee the correctness of the provided techniques. We conduct experiments on widely-used machine learning datasets. The experiment results demonstrate that the proposed method significantly enhances computational efficiency while preserving model evaluation accuracy and offering strong stability for TWD thresholds. This paper provides a validation tool and reasoning method for dealing with imbalanced data in TWD.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122016"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

{"title":"RIONIDA: A novel algorithm for imbalanced data combining instance-based learning and rule induction","authors":"Grzegorz Góra ,&nbsp;Andrzej Skowron","doi":"10.1016/j.ins.2025.122015","DOIUrl":"10.1016/j.ins.2025.122015","url":null,"abstract":"<div><div>The article presents the Rule Induction with Optimal Neighbourhood for Imbalanced Data Algorithm (RIONIDA) learning algorithm based on combination of two widely-used empirical approaches: rule induction and instance-based learning for imbalanced data classification. The algorithm is a substantial extension of the well-known the Rule Induction with Optimal Neighbourhood Algorithm (RIONA) learning algorithm developed for balanced data.</div><div>RIONIDA uses rules more general than in RIONA and realises a few additional concepts in comparison to RIONA, i.e. optimisation of the explicitly given performance measure defined over the confusion matrix, optimisation of weights for two classes, the idea of scaled rules, optimisation of parameters related to two latter ideas. RIONIDA, with decisions explainable for the user, is relatively fast and significantly outperforms the state-of-the-art algorithms analysed in the article.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122015"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549538","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":"EVA: Key values eclosion with space anchor used in hand pose estimation and shape reconstruction","authors":"Xuefeng Li ,&nbsp;Xiangbo Lin","doi":"10.1016/j.ins.2025.122003","DOIUrl":"10.1016/j.ins.2025.122003","url":null,"abstract":"<div><div>3D hand pose estimation and shape reconstruction from single RGB image face challenges of self-occlusion, object occlusion, and depth ambiguity. Previous methods tried efforts to detect relevant information from images directly. Differently, this paper considers the task as a union of detection and generation. A novel framework called Key Value Eclosion is proposed. It utilizes powerful Diffusion generation strategies to gradually generate and refine occluded joints, vertices, and depth, using visible 2D joint locations as clues. To make the latent codes more comprehensive for hand shape reconstruction, 2D image features are transformed into 3D space using the proposed Space Anchor based feature inverse projection strategy. Integrating the Space Anchor based feature inverse projection into the Key Values Eclosion framework, a complete hand pose estimation and shape reconstruction model called EVA is constructed. The EVA model demonstrates excellent accuracy on both aligned and unaligned metrics using the HO-3D and DexYCB datasets. Especially, the improvement on Mean Error and Trans&amp;Scale metrics are about 30%~50%, compared to state-of-the-art methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122003"},"PeriodicalIF":8.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488959","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":"JLR-GCN: Joint label-aware and relation-aware graph convolution neural network for heterogeneous graph representations","authors":"Zhenquan Shi,&nbsp;Wengjian Zhang,&nbsp;Jiashuang Huang,&nbsp;Weiping Ding","doi":"10.1016/j.ins.2025.122011","DOIUrl":"10.1016/j.ins.2025.122011","url":null,"abstract":"<div><div>Node representation learning on heterogeneous graphs has garnered increasing attention. However, most existing heterogeneous graph learning models primarily focus on improving the performance of neural networks while overlooking two crucial aspects. On the one hand, they do not give sufficient importance to the value of label information in heterogeneous graph representation learning and merely use it for loss computation. On the other hand, they neglect the differences in heterogeneous structural information carried by interactions between nodes of different relation types. To address these challenges, this paper proposes a joint label-aware and relation-aware graph convolution neural network (JLR-GCN) for heterogeneous graph representations. Our model enhances the expressive power of the heterogeneous graph by leveraging label attributes and structural information. Label-aware and relation-aware are combined to explore the differences in the influence of each relation type during the heterogeneous graph representation learning process. Moreover, a stacked graph convolutional network with <em>meta</em>-path importance parameters is adopted for heterogeneous message passing to capture long and short <em>meta</em>-paths. The allocation of network layer outputs based on their importance is adjusted, enabling the effective learning of node embeddings by incorporating valuable heterogeneous relation, node attributes, and structural signals. We compared our model with state-of-the-art heterogeneous graph representation models on three different real-world datasets, and the results demonstrated that our model significantly outperformed the best baseline. The maximum performance improvement achieved was 16.14%.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122011"},"PeriodicalIF":8.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510578","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":"Toward trustworthy identity tracing via multi-attribute synergistic identification","authors":"Wenbin Feng ,&nbsp;Decheng Liu ,&nbsp;Ruimin Hu ,&nbsp;Jiahao Yu","doi":"10.1016/j.ins.2025.122012","DOIUrl":"10.1016/j.ins.2025.122012","url":null,"abstract":"<div><div>Identity tracing is a method used to determine the true identity of a subject by selecting and analyzing its relevant attributes, and it is one of the most important foundational issues in the field of social security prevention. However, traditional identity recognition technologies based on single attributes have difficulty achieving ultimate recognition accuracy, whereas deep learning-based models tend to lack interpretability. Collaborative identification using multiple attributes offers a viable approach to address these limitations and resolve issues related to data quality. In this paper, we propose the “Trustworthy Identity Tracing” (TIT) task and a Multi-attribute Synergistic Identification based TIT framework. We first established a novel identity model based on identity entropy theoretically. The individual conditional identity entropy and core identification set are defined to reveal the intrinsic mechanism of multivariate attribute collaborative identification. Based on the proposed identity model, we propose a trustworthy identity tracing framework (TITF) with multi-attribute synergistic identification to determine the identity of unknown objects, which can optimize the core identification set and provide an interpretable identity tracing process. Actually, the essence of identity tracing is revealed to be the process of the identity entropy value converging to zero. To compensate for the lack of test data, we construct a dataset of 5000 objects to simulate real-world scenarios, where 20 identity attributes are labeled to trace unknown object identities. The experiment results conducted on the mentioned dataset show the proposed TITF algorithm can achieve satisfactory identification performance. Furthermore, the proposed TIT task explores the interpretable quantitative mathematical relationship between attributes and identity, broadening identity representation from a single-attribute domain to a multi-attribute collaborative domain. It indeed provides a novel perspective for realizing credible and interpretable identification techniques in real-world scenarios.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122012"},"PeriodicalIF":8.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512608","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":"Optimizing semiconductor process recipe settings using hybrid meta-learning and metaheuristic approaches","authors":"Zhen-Yin Annie Chen ,&nbsp;Chun-Cheng Lin ,&nbsp;Ke-Wen Lu","doi":"10.1016/j.ins.2025.121998","DOIUrl":"10.1016/j.ins.2025.121998","url":null,"abstract":"<div><div>Achieving high-yield production in semiconductor thin film chemical vapor deposition (CVD) processes requires precise parameter settings, which often rely on costly R&amp;D efforts and expert experience. This work proposes a novel approach combining meta-learning with metaheuristic algorithms to optimize these parameters more efficiently, particularly when experimental data is limited. Collaborated with a semiconductor manufacturer specializing in low-volume, high-variety products, we addressed the challenge of limited experimental data. To optimize new product processes, the company collected limited real experiment data. However, conventional soft computing methods often require extensive data for accurate prediction models, increasing computational time and costs. Therefore, we propose hybrid meta-learning and metaheuristic approaches to efficiently determine parameter settings. Meta-learning leverages historical data from similar tasks to train a neural acquisition function within the meta Bayesian optimization (MetaBO) framework. Two enhancements are proposed: employing the Halton sequence to reduce computational complexity and integrating three metaheuristic algorithms (genetic algorithm, particle swarm optimization PSO, and artificial fish school algorithm) to refine evaluation points and improve model quality. Experiments on benchmarking black-box functions and real semiconductor CVD processes show superior performance of our approaches over legacy MetaBO and other acquisition function methods, with the PSO-incorporated hybrid approach performing best.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 121998"},"PeriodicalIF":8.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510577","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":"Co-location pattern mining using approximate Euclidean measure","authors":"W. Andrzejewski,&nbsp;P. Boinski","doi":"10.1016/j.ins.2025.122000","DOIUrl":"10.1016/j.ins.2025.122000","url":null,"abstract":"<div><div>Co-location discovery plays an important role in spatial data mining. It aims to find types of objects that are frequently located together in a spatial neighborhood. A very popular interestingness measure for co-locations requires knowledge of all objects participating in co-location instances. A common requirement for co-location instances in the literature is that all objects contained in them are pairwise neighbors. Typically, this is determined in quadratic time with respect to the number of objects. In this paper, we introduce a new framework for determining pairwise neighborhoods in linear time. The framework utilizes a new metric that generates approximately the same neighborhoods as the Euclidean metric. We provide modifications of two algorithms for co-location instance identification that employ the proposed approach. Experiments performed on two real-world datasets demonstrate that we can achieve better processing times than using the state-of-the-art approach.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122000"},"PeriodicalIF":8.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510576","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 method for determining maximin OWA operator weights","authors":"Byeong Seok Ahn","doi":"10.1016/j.ins.2025.122010","DOIUrl":"10.1016/j.ins.2025.122010","url":null,"abstract":"<div><div>In this paper, we present a mathematical programming-based approach to determine the ordered weighted averaging (OWA) operator weights by maximizing the smallest difference between adjacent weights, referred to as the <em>maximin</em> OWA operator weights. Behavioral evidence suggests that decision-makers’ implicit weights, which influence their choices, are often quite steep. Thus, they tend to intuitively prefer alternatives that excel in several important criteria. If one alternative’s score is comparable to others, they might consider secondary and tertiary important criteria. The proposed maximin approach aligns more closely with this philosophy compared to previous methods that aim to evenly distribute operator weights.</div><div>Furthermore, we derive a closed-form solution for the maximin OWA operator weights using results from convex analysis. We also revisit the minimax disparity model, which is similar to our maximin approach, to emphasize the similarities and differences between the two methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122010"},"PeriodicalIF":8.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534520","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}