IEEE Transactions on Big Data最新文献_第10页

Data-Centric Graph Learning: A Survey 以数据为中心的图学习：综述

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-06 DOI: 10.1109/TBDATA.2024.3489412

Yuxin Guo;Deyu Bo;Cheng Yang;Zhiyuan Lu;Zhongjian Zhang;Jixi Liu;Yufei Peng;Chuan Shi

{"title":"Data-Centric Graph Learning: A Survey","authors":"Yuxin Guo;Deyu Bo;Cheng Yang;Zhiyuan Lu;Zhongjian Zhang;Jixi Liu;Yufei Peng;Chuan Shi","doi":"10.1109/TBDATA.2024.3489412","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489412","url":null,"abstract":"The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models. Graph learning, which operates on ubiquitous topological data, also plays an important role in the era of deep learning. In this survey, we comprehensively review graph learning approaches from the data-centric perspective, and aim to answer three crucial questions: <italic>(1) when to modify graph data</i>, <italic>(2) what part of the graph data needs modification</i> to unlock the potential of various graph models, and <italic>(3) how to safeguard graph models</i> from problematic data influence. Accordingly, we propose a novel taxonomy based on the stages in the graph learning pipeline, and highlight the processing methods for different data structures in the graph data, i.e., topology, feature and label. Furthermore, we analyze some potential problems embedded in graph data and discuss how to solve them in a data-centric manner. Finally, we provide some promising future directions for data-centric graph learning.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"1-20"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AnesFormer: An End-to-End Framework for EEG-Based Anesthetic State Classification AnesFormer：基于脑电图的麻醉状态分类的端到端框架

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-01 DOI: 10.1109/TBDATA.2024.3489419

Qihang Wang;Ying Chen;Qinge Xiao

{"title":"AnesFormer: An End-to-End Framework for EEG-Based Anesthetic State Classification","authors":"Qihang Wang;Ying Chen;Qinge Xiao","doi":"10.1109/TBDATA.2024.3489419","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489419","url":null,"abstract":"To determine the real-time changes in brain arousal introduced by anesthetics, Electroencephalogram (EEG) is often used as an objective neuroimaging evidence to link the neurobehavioral states of patients. However, EEG signals often suffer from a low signal-to-noise ratio due to environmental noise and artifacts, which limits its application for a reliable estimation of depth of anesthesia (DoA), especially under high cross-subject variability. In this study, we propose an end-to-end deep learning based framework, termed as AnesFormer, which contains a data selection model, a self-attention based classification model, and a baseline update mechanism. These three components are integrated in a dynamic and seamless manner to achieve the goal of improving the effectiveness and robustness of DoA estimation in a leave-one-out setting. In the experiment, we apply the proposed framework to an office-based dataset and a hospital-based dataset, and use seven existing models as benchmarks. In addition, we conduct an ablation experiment to show the significance of each component in AnesFormer. Our main results indicate that 1) the proposed framework generally performs better than the existing methods for DoA estimation in terms of effectiveness and robustness; 2) each designed component in AnesFormer is likely to contribute to the DoA classification improvement.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1357-1368"},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stable Learning via Dual Feature Learning 通过双特征学习实现稳定学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-01 DOI: 10.1109/TBDATA.2024.3489413

Shuai Yang;Xin Li;Minzhi Wu;Qianlong Dang;Lichuan Gu

{"title":"Stable Learning via Dual Feature Learning","authors":"Shuai Yang;Xin Li;Minzhi Wu;Qianlong Dang;Lichuan Gu","doi":"10.1109/TBDATA.2024.3489413","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489413","url":null,"abstract":"Stable learning aims to leverage the knowledge in a relevant source domain to learn a prediction model that can generalize well to target domains. Recent advances in stable learning mainly proceed by eliminating spurious correlations between irrelevant features and labels through sample reweighting or causal feature selection. However, most existing stable learning methods either only weaken partial spurious correlations or discard part of true causal relationships, resulting in generalization performance degradation. To tackle these issues, we propose the Dual Feature Learning (DFL) algorithm for stable learning, which consists of two phases. Phase 1 first learns a set of sample weights to balance the distribution of treated and control groups corresponding to each feature, and then uses the learned sample weights to assist feature selection to identify part of irrelevant features for completely isolating spurious correlations between these irrelevant features and labels. Phase 2 first learns two groups of sample weights again using the subdataset after feature selection, and then obtains high-quality feature representations by integrating a weighted cross-entropy model and an autoencoder model to further get rid of spurious correlations. Using synthetic and four real-world datasets, the experiments have verified the effectiveness of DFL, in comparison with eleven state-of-the-art methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1852-1866"},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

M-Graphormer: Multi-Channel Graph Transformer for Node Representation Learning m - graphhormer：用于节点表示学习的多通道图转换器

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-01 DOI: 10.1109/TBDATA.2024.3489418

Xinglong Chang;Jianrong Wang;Mingxiang Wen;Yingkui Wang;Yuxiao Huang

{"title":"M-Graphormer: Multi-Channel Graph Transformer for Node Representation Learning","authors":"Xinglong Chang;Jianrong Wang;Mingxiang Wen;Yingkui Wang;Yuxiao Huang","doi":"10.1109/TBDATA.2024.3489418","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489418","url":null,"abstract":"In recent years, the Graph Transformer has demonstrated superiority on various graph-level tasks by facilitating global interactions among nodes. However, as for node-level tasks, the existing Graph Transformer cannot perform as well as expected. Actually, a node in a real-world graph does not necessarily have relationships with every other node, and this global interaction weakens node features. This raises a fundamental question: should we partition out an appropriate interaction channel based on graph structure so that noisy and irrelevant information will be filtered and every node can aggregate information in the optimal channel? We first perform a series of experiments on manually created graphs with varying homophily ratios. Surprisingly, we observe that different graph structures indeed require distinct optimal interaction channels. This leads us to ask whether we can develop a partitioning rule that ensures each node interacts with relevant and valuable targets. To overcome this challenge, we propose a novel Graph Transformer named Multi-channel Graphormer. The model is evaluated on six network datasets with different homophily ratios for the node classification task. Moreover, comprehensive experiments are conducted on two real datasets for the recommendation task. Experimental results show that the Multi-channel Graphormer surpasses state-of-the-art baselines, demonstrating superior performance.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1867-1878"},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Big Data Analysis for Industrial Activity Recognition Using Attention-Inspired Sequential Temporal Convolution Network 基于注意力启发时序时序卷积网络的工业活动识别大数据分析

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-01 DOI: 10.1109/TBDATA.2024.3489414

Altaf Hussain;Tanveer Hussain;Waseem Ullah;Samee Ullah Khan;Min Je Kim;Khan Muhammad;Javier Del Ser;Sung Wook Baik

{"title":"Big Data Analysis for Industrial Activity Recognition Using Attention-Inspired Sequential Temporal Convolution Network","authors":"Altaf Hussain;Tanveer Hussain;Waseem Ullah;Samee Ullah Khan;Min Je Kim;Khan Muhammad;Javier Del Ser;Sung Wook Baik","doi":"10.1109/TBDATA.2024.3489414","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489414","url":null,"abstract":"Deep-learning-based human activity recognition (HAR) methods have significantly transformed a wide range of domains over recent years. However, the adoption of Big Data techniques in industrial applications remains challenging due to issues such as generalized weight optimization, diverse viewpoints, and the complex spatiotemporal features of videos. To address these challenges, this work presents an industrial HAR framework consisting of two main phases. First, a squeeze bottleneck attention block (SBAB) is introduced to enhance the learning capabilities of the backbone model for contextual learning, which allows for the selection and refinement of an optimal feature vector. In the second phase, we propose an effective sequential temporal convolutional network (STCN), which is designed in parallel fashion to mitigate the issues of exploding and vanishing gradients associated with sequence learning. The high-dimensional spatiotemporal feature vectors from the STCN undergo further refinement through our proposed SBAB in a sequential manner, to optimize the features for HAR and enhance the overall performance. The efficacy of the proposed framework is validated through extensive experiments on six datasets, including data from industrial and general activities.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1840-1851"},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Radio Map Construction With Minimal Manual Intervention: A State Space Model-Based Approach With Imitation Learning 最小人工干预的动态无线电地图构建：一种基于状态空间模型的模仿学习方法

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-10-31 DOI: 10.1109/TBDATA.2024.3489425

Xiaoqiang Zhu;Tie Qiu;Wenyu Qu;Xiaobo Zhou;Tuo Shi;Tianyi Xu

{"title":"Dynamic Radio Map Construction With Minimal Manual Intervention: A State Space Model-Based Approach With Imitation Learning","authors":"Xiaoqiang Zhu;Tie Qiu;Wenyu Qu;Xiaobo Zhou;Tuo Shi;Tianyi Xu","doi":"10.1109/TBDATA.2024.3489425","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489425","url":null,"abstract":"Fingerprint localization methods typically require a substantial amount of manual effort to collect fingerprint data from various scenarios to construct an accurate radio map. While some existing research has attempted to use path planning strategies to save on labor costs, these approaches often suffer from being time-consuming and prone to locally optimal solutions. To address these shortcomings, our paper proposes a novel approach that utilizes imitation learning to construct and update a highly accurate radio map with minimal manual intervention in dynamic environments. Specifically, we employ a multivariate Gaussian process model to fit a rough standby fingerprint database with only a few pilot data points. We then utilize a state space model to calculate the variation range of the pilot data, which forms the CSI error band used to filter the rough radio map. Imitation learning and a confidence coefficient are utilized to predict and calibrate the global CSI data distribution. And we utilize the K-nearest neighbor algorithm to achieve the real-time localization function. Experimental results show that our proposed algorithm outperforms several state-of-the-art approaches in most test cases, exhibiting low computation complexity, lower localization error, and saving 73.3% of the manual workload.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1799-1812"},"PeriodicalIF":7.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-Based Complex Logical Query on Temporal Knowledge Graph via Graph Neural Network 基于图神经网络的时态知识图的复杂逻辑查询

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-10-31 DOI: 10.1109/TBDATA.2024.3489421

Luyi Bai;Linshuo Xu;Lin Zhu

{"title":"Attention-Based Complex Logical Query on Temporal Knowledge Graph via Graph Neural Network","authors":"Luyi Bai;Linshuo Xu;Lin Zhu","doi":"10.1109/TBDATA.2024.3489421","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489421","url":null,"abstract":"Answering complex logical queries on large-scale Knowledge Graphs (KGs) efficiently and accurately has always been crucial for question-answering systems. Recent studies have significantly improved the performance of complex logical queries on massive knowledge graphs by leveraging graph neural networks (GNNs). However, the existing GNN-based methods still have limitations in dealing with long-sequence logical queries. They usually decompose complex queries into multiple independent first-order logical queries, which leads to the inability to optimize globally, and the query accuracy will drop sharply with the increase of query length. In addition, the knowlege in the real world is dynamically changing, but most of the existing methods are more suitable for dealing with static knowledge graphs, and there is still much room for improvement when dealing with logical queries in temporal knowledge graphs. In this paper, we propose a novel Temporal Complex Logical Query (TCLQ) model to achieve temporal logical queries on temporal knowledge graphs. We add time series embedding into GNN, and use multi-layer GRUs to aggregate the node features of previous time and current time, which effectively enhances the time series reasoning ability of the model. In order to solve the problem that the accuracy of logical query model decreases significantly with the increase of query sequence length, we establish a multi-level attention coefficients model to learn and optimize the whole logical queries, thus reducing the error accumulation problem when the queries are decomposed into multiple independent first-order logical queries. We conduct experiments on multiple temporal datasets and demonstrate the effectiveness of TCLQ.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1828-1839"},"PeriodicalIF":7.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DGNN: Decoupled Graph Neural Networks With Structural Consistency Between Attribute and Graph Embedding Representations 在属性和图嵌入表示之间具有结构一致性的解耦图神经网络

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-10-31 DOI: 10.1109/TBDATA.2024.3489420

Jinlu Wang;Jipeng Guo;Yanfeng Sun;Junbin Gao;Shaofan Wang;Yachao Yang;Baocai Yin

{"title":"DGNN: Decoupled Graph Neural Networks With Structural Consistency Between Attribute and Graph Embedding Representations","authors":"Jinlu Wang;Jipeng Guo;Yanfeng Sun;Junbin Gao;Shaofan Wang;Yachao Yang;Baocai Yin","doi":"10.1109/TBDATA.2024.3489420","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489420","url":null,"abstract":"Graph neural networks (GNNs) exhibit a robust capability for representation learning on graphs with complex structures, demonstrating superior performance across various applications. Most existing GNNs utilize graph convolution operations that integrate both attribute and structural information through coupled way. And these GNNs, from an optimization perspective, seek to learn a consensus and compromised embedding representation that balances attribute and graph information, selectively exploring and retaining valid information in essence. To obtain a more comprehensive embedding representation, a novel GNN framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced. DGNN separately explores distinctive embedding representations from the attribute and graph spaces by decoupled terms. Considering that the semantic graph, derived from attribute feature space, contains different node connection information and provides enhancement for the topological graph, both topological and semantic graphs are integrated by DGNN for powerful embedding representation learning. Further, structural consistency between the attribute embedding and the graph embedding is promoted to effectively eliminate redundant information and establish soft connection. This process involves facilitating factor sharing for adjacency matrices reconstruction, which aims at exploring consensus and high-level correlations. Finally, a more powerful and comprehensive representation is achieved through the concatenation of these embeddings. Experimental results conducted on several graph benchmark datasets demonstrate its superiority in node classification tasks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1813-1827"},"PeriodicalIF":7.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reward Shaping Based on Optimal-Policy-Free 基于Optimal-Policy-Free的奖励塑造

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-10-31 DOI: 10.1109/TBDATA.2024.3489415

Jianghui Sang;Yongli Wang;Zaki Ahmad Khan;Xiaoliang Zhou

{"title":"Reward Shaping Based on Optimal-Policy-Free","authors":"Jianghui Sang;Yongli Wang;Zaki Ahmad Khan;Xiaoliang Zhou","doi":"10.1109/TBDATA.2024.3489415","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489415","url":null,"abstract":"Existing research on potential-based reward shaping (PBRS) relies on optimal policy in Markov decision process (MDP) where optimal policy is regarded as the ground truth. However, in some practical application scenarios, there is an extrapolation error challenge between the computed optimal policy and the real-world optimal policy. At this time, the optimal policy is unreliable. To address this challenge, we design a Reward Shaping based on Optimal-Policy-Free to get rid of the dependence on the optimal policy. We view reinforcement learning as probabilistic inference on a directed graph. Essentially, this inference propagates information from the rewarding states in the MDP and results in a function which is leveraged as a potential function for PBRS. Our approach utilizes a contrastive learning technique on directed graph Laplacian. Here, this technique does not change the structure of the directed graph. Then, the directed graph Laplacian is used to approximate the true state transition matrix in MDP. The potential function in PBRS can be learned through the message passing mechanism which is built on this directed graph Laplacian. The experiments on Atari, MuJoCo and MiniWorld show that our approach outperforms the competitive algorithms.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1787-1798"},"PeriodicalIF":7.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AFML: An Asynchronous Federated Meta-Learning Mechanism for Charging Station Occupancy Prediction With Biased and Isolated Data 基于有偏差和孤立数据的充电站占用预测的异步联邦元学习机制

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-10-22 DOI: 10.1109/TBDATA.2024.3484651

Qiyang Chen;Linlin You;Haohao Qu;Ahmed M. Abdelmoniem;Chau Yuen

{"title":"AFML: An Asynchronous Federated Meta-Learning Mechanism for Charging Station Occupancy Prediction With Biased and Isolated Data","authors":"Qiyang Chen;Linlin You;Haohao Qu;Ahmed M. Abdelmoniem;Chau Yuen","doi":"10.1109/TBDATA.2024.3484651","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3484651","url":null,"abstract":"Electric vehicles (EVs) are driving green and low-carbon transport in modern cities. It makes charging station occupancy prediction (CSOP) critual for intelligent transportation systems (ITS) to achieve a balance between the supply and demand in resolving the dynamics between EVs and changing stations. Even though several Big Data-based solutions have been discussed, they are still struggling to collaboratively utilize heterogeneous data and distributed computing resources located at both physically and logicially isolated charging stations to better support context-driven CSOP. To addres this challenge, we propose an Asynchronous Federated Meta-learning Mechanism (AFML) for CSOP, which can train a meta-model with strong adaptation ability in an asynchronous and collaborative manner. In general, it incorporates an adaptive reptile algorithm (AR) and an weighted aggregation strategy (WA) to jointly ensure the training efficiency and model adaptivity. Evaluations on real-world CSOP datasets demonstrate that compared to the second best method, AFML can significantly improve forecasting accuracy by 14%, accelerate model convergence by 9% and enhance model generalizability by 10%, illustrating its merits in support CSOP to embrace a smart and sustainable city.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1772-1786"},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0