IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第6页

MENTOR: Guiding Hierarchical Reinforcement Learning With Human Feedback and Dynamic Distance Constraint 指导层次强化学习与人的反馈和动态距离约束

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-28 DOI: 10.1109/TETCI.2025.3529902

Xinglin Zhou;Yifu Yuan;Shaofu Yang;Jianye Hao

{"title":"MENTOR: Guiding Hierarchical Reinforcement Learning With Human Feedback and Dynamic Distance Constraint","authors":"Xinglin Zhou;Yifu Yuan;Shaofu Yang;Jianye Hao","doi":"10.1109/TETCI.2025.3529902","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529902","url":null,"abstract":"Hierarchical reinforcement learning (HRL) provides a promising solution for complex tasks with sparse rewards of agents, which uses a hierarchical framework that divides tasks into subgoals and completes them sequentially. However, current methods struggle to find suitable subgoals for ensuring a stable learning process. To address the issue, we propose a general hierarchical reinforcement learning framework incorporating human feedback and dynamic distance constraints, termed <bold>MENTOR</b>, which acts as a “<italic>mentor</i>”. Specifically, human feedback is incorporated into high-level policy learning to find better subgoals. Furthermore, we propose the Dynamic Distance Constraint (DDC) mechanism dynamically adjusting the space of optional subgoals, such that MENTOR can generate subgoals matching the low-level policy learning process from easy to hard. As a result, the learning efficiency can be improved. As for low-level policy, a dual policy is designed for exploration-exploitation decoupling to stabilize the training process. Extensive experiments demonstrate that MENTOR uses a small amount of human feedback to achieve significant improvement in complex tasks with sparse rewards.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1292-1306"},"PeriodicalIF":5.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716412","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

A Many-Objective Diversity-Guided Differential Evolution Algorithm for Multi-Label Feature Selection in High-Dimensional Datasets 高维数据集多标签特征选择的多目标多样性导向差分进化算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-27 DOI: 10.1109/TETCI.2025.3529840

Emrah Hancer;Bing Xue;Mengjie Zhang

{"title":"A Many-Objective Diversity-Guided Differential Evolution Algorithm for Multi-Label Feature Selection in High-Dimensional Datasets","authors":"Emrah Hancer;Bing Xue;Mengjie Zhang","doi":"10.1109/TETCI.2025.3529840","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529840","url":null,"abstract":"Multi-label classification (MLC) is crucial as it allows for a more nuanced and realistic representation of complex real-world scenarios, where instances may belong to multiple categories simultaneously, providing a comprehensive understanding of the data. Effective feature selection in MLC is paramount as it cannot only enhance model efficiency and interpretability but also mitigate the curse of dimensionality, ensuring more accurate and streamlined predictions for complex, multi-label data. Despite the proven efficacy of evolutionary computation (EC) techniques in enhancing feature selection for multi-label datasets, research on feature selection in MLC remains sparse in the domain of multi- and many-objective optimization. This paper proposes a many-objective differential evolution algorithm called MODivDE for feature selection in high-dimensional MLC tasks. The MODivDE algorithm involves multiple improvements and innovations in quality indicator-based selection, logic-based search strategy, and diversity-based archive update. The results demonstrate the exceptional performance of the MODivDE algorithm across a diverse range of high-dimensional datasets, surpassing recently introduced many-objective and conventional multi-label feature selection algorithms. The advancements in MODivDE collectively contribute to significantly improved accuracy, efficiency, and interpretability compared to state-of-the-art methods in the realm of multi-label feature selection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1226-1237"},"PeriodicalIF":5.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716518","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

Aspect-Aware Graph Interaction Attention Network for Aspect Category Sentiment Analysis 面向方面类别情感分析的方面感知图交互注意网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-27 DOI: 10.1109/TETCI.2025.3526285

Pengfei Yu;Jingjing Gu;Dechang Pi;Qiang Zhou;Qiuhong Wang

{"title":"Aspect-Aware Graph Interaction Attention Network for Aspect Category Sentiment Analysis","authors":"Pengfei Yu;Jingjing Gu;Dechang Pi;Qiang Zhou;Qiuhong Wang","doi":"10.1109/TETCI.2025.3526285","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526285","url":null,"abstract":"This paper explores an implicit Aspect Category Sentiment Analysis task, which aims to determine the sentiment polarities of given aspect categories in social reviews. Currently, most researchers focus more on explicit aspect and rarely work on implicit aspect. Meanwhile, due to the semantic complexity of natural language, it is difficult for existing methods to retrieve such implicit semantics in sentences. To this end, we propose a novel framework, the Aspect-aware Graph Interaction Attention Network (AGIAN), which concentrates on aspect-related information implicitly in sentences and identifies its corresponding sentiment polarity. Specifically, first, we introduce an aspect-aware graph to represent potential associations between the implicit aspect category and the sentence. Then, we utilize two types of graph neural networks to extract rich relational semantics. Finally, we design a graph interaction mechanism to integrate sentiment features specific to the aspect category for sentiment classification. We evaluate the performance of the proposed framework on six publicly available benchmark datasets. Extensive experiments demonstrate that, compared to some competitive baseline methods, AGIAN can effectively improve accuracy and achieve state-of-the-art performance on the F1-score.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3122-3135"},"PeriodicalIF":5.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687778","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

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-23 DOI: 10.1109/TETCI.2025.3529608

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-23 DOI: 10.1109/TETCI.2025.3529610

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-23 DOI: 10.1109/TETCI.2025.3529606

引用次数: 0

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach 增强不平衡学习：一种新的松弛因子模糊支持向量机方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-22 DOI: 10.1109/TETCI.2024.3524718

M. Tanveer;Anushka Tiwari;Mushir Akhtar;Chin-Teng Lin

{"title":"Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach","authors":"M. Tanveer;Anushka Tiwari;Mushir Akhtar;Chin-Teng Lin","doi":"10.1109/TETCI.2024.3524718","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3524718","url":null,"abstract":"In real-world applications, class-imbalanced datasets pose significant challenges for machine learning algorithms, such as support vector machines (SVMs), particularly in effectively managing imbalance, noise, and outliers. Fuzzy support vector machines (FSVMs) address class imbalance by assigning varying fuzzy memberships to samples; however, their sensitivity to imbalanced datasets can lead to inaccurate assessments. The recently developed slack-factor-based FSVM (SFFSVM) improves traditional FSVMs by using slack factors to adjust fuzzy memberships based on misclassification likelihood, thereby rectifying misclassifications induced by the hyperplane obtained via different error cost (DEC). Building on SFFSVM, we propose an improved slack-factor-based FSVM (ISFFSVM) that introduces a novel location parameter. This novel parameter significantly advances the model by constraining the DEC hyperplane's extension, thereby mitigating the risk of misclassifying minority class samples. It ensures that majority class samples with slack factor scores approaching the location threshold are assigned lower fuzzy memberships, which enhances the model's discrimination capability. Extensive experimentation on a diverse array of real-world KEEL datasets demonstrates that the proposed ISFFSVM consistently achieves higher F1-scores, Matthews correlation coefficients (MCC), and area under the precision-recall curve (AUC-PR) compared to baseline classifiers. Consequently, the introduction of the location parameter, coupled with the slack-factor-based fuzzy membership, enables ISFFSVM to outperform traditional approaches, particularly in scenarios characterized by severe class disparity.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3112-3121"},"PeriodicalIF":5.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687733","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

K-Core Structure Feature Encoding-Based Enhanced Federated Graph Learning Framework 基于k核结构特征编码的增强联邦图学习框架

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-20 DOI: 10.1109/TETCI.2025.3526278

Dongdong Li;Bo Liu;Chunqiao Yang;Fang Shi;Yunfei Peng;Weiwei Lin

{"title":"K-Core Structure Feature Encoding-Based Enhanced Federated Graph Learning Framework","authors":"Dongdong Li;Bo Liu;Chunqiao Yang;Fang Shi;Yunfei Peng;Weiwei Lin","doi":"10.1109/TETCI.2025.3526278","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526278","url":null,"abstract":"Federated Graph Learning (FGL) demonstrates tremendous potential in distributed graph data analysis and modeling. The rapid growth of graph data and the increasing awareness of privacy protection make FGL research highly valuable. However, its development faces two critical challenges: the non-IID problem in heterogeneous graphs and low communication efficiency. This study proposes an Enhanced FGL framework based on K-core Structure Feature Encoding (FedKcore) to utilize various heterogeneous graphs efficiently. The nested chain structure containing rich information and linear encoding time make K-core structural attributes highly suitable for graph enhancement and aggregate sharing on edge devices. Client personalization capabilities are enhanced by combining original features with K-core attributes for local training. To improve convergence speed and overcome the non-IID challenge, we aggregate and share only the learnable parameters related to K-core attributes. Upon this, the introduced Circle Loss function optimizes feature space and boundaries, enhancing the performance of K-core attributes. Extensive experiments on heterogeneous graphs show that, compared to the state-of-the-art FedStar, FedKcore improves accuracy by over 1.3% and speeds up convergence by 1.3 times.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3097-3111"},"PeriodicalIF":5.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687779","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

PCR: A Parallel Convolution Residual Network for Traffic Flow Prediction PCR：一种用于交通流预测的并行卷积残差网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-16 DOI: 10.1109/TETCI.2025.3525656

Changqi Zuo;Xu Zhang;Gen Zhao;Liang Yan

{"title":"PCR: A Parallel Convolution Residual Network for Traffic Flow Prediction","authors":"Changqi Zuo;Xu Zhang;Gen Zhao;Liang Yan","doi":"10.1109/TETCI.2025.3525656","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3525656","url":null,"abstract":"Traffic flow prediction is crucial in smart cities and traffic management, yet it presents challenges due to intricate spatial-temporal dependencies and external factors. Most existing research relied on a traditional data selection approach to represent temporal dependence. However, only considering spatial dependence in adjacent or distant regions limits the performance. In this paper, we propose an end-to-end Parallel Convolution Residual network (PCR) for grid-based traffic flow prediction. First, we introduce a novel data selection strategy to capture more temporal dependence, and then we implement an early fusion strategy without any additional operations to obtain a lighter model. Second, we propose to extract external features with feature embedding matrix operations, which can represent the interrelationships between different kinds of external data. Finally, we build a parallel residual network with concatenated features as input, which is composed of a standard residual net (SRN) to extract short spatial dependence and a dilated residual net (DRN) to extract long spatial dependence. Experiments on three traffic flow datasets TaxiBJ, BikeNYC, and TaxiCQ exhibit that the proposed method outperforms the state-of-the-art models with the most minor parameters.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3072-3083"},"PeriodicalIF":5.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687731","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

An Efficient Sampling Approach to Offspring Generation for Evolutionary Large-Scale Constrained Multi-Objective Optimization 进化大规模约束多目标优化子代生成的有效抽样方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-16 DOI: 10.1109/TETCI.2025.3526268

Langchun Si;Xingyi Zhang;Yajie Zhang;Shangshang Yang;Ye Tian

{"title":"An Efficient Sampling Approach to Offspring Generation for Evolutionary Large-Scale Constrained Multi-Objective Optimization","authors":"Langchun Si;Xingyi Zhang;Yajie Zhang;Shangshang Yang;Ye Tian","doi":"10.1109/TETCI.2025.3526268","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526268","url":null,"abstract":"Constrained multi-objective evolutionary algorithms have been extensively used for solving real-world problems. However, most algorithms struggle to efficiently find feasible solutions when the problem involves massive decision variables and decision space constraints. To tackle this issue, an efficient sampling approach is suggested to guide the offspring generation, where three types of directions are utilized according to the status of the current population, including the feasibility-preferred direction, the convergence-preferred direction, and the diversity-preferred direction. Specifically, the proposed approach adopts the feasibility-preferred direction to guide solutions towards constraint satisfaction when most solutions are infeasible, whereas the convergence-preferred direction is utilized to guide solutions to approach the optimal set when most solutions are dominated, and the diversity-preferred direction is employed to spread solutions to cover the optimal set when most solutions are non-dominated. Besides, a reinforcement learning approach is proposed to automatically determine the constraint handling technique in each iteration. With the proposed approaches, a large-scale constrained multi-objective evolutionary algorithm is also developed. The experiment is conducted on 31 benchmark problems with 1000 dimensions and five real-world problems with dimensions varying from 1170 to 2610, and experimental results reveal the competitive effectiveness of the proposed algorithm.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2080-2092"},"PeriodicalIF":5.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148135","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