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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective optimization

IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Shuaizheng Feng, Chaolu Li, Wei Yang, Jinzhu Zhao, Dazhe Northeastern University School of Computer Science And Engineering Shenyang China Ministry of Education Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

ISBN: (纸本)9798350386226

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction. © 2024 IEEE.

关键词： Self-supervised learning

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SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning 27

SETTP: Style Extraction and Tunable Inference via Dual-Level...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaïane, Osmar Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

ISBN: (纸本)9781643685489

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. © 2024 The Authors.

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Towards Time-Variant-Aware Link Prediction in Dynamic Graph Through Self-supervised Learning 9th

Towards Time-Variant-Aware Link Prediction in Dynamic Graph...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wen, Guangqi Cao, Peng Jin, Zhiyong Song, Ruoxian Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Dynamic graph link prediction is a challenging problem because the graph topology and node attributes vary at different times. A purely supervised learning scheme for the dynamic graph data usually leads to poor generalization due to insufficient supervision. As a promising solution, self-supervised learning can be introduced to dynamic graph analysis tasks. However, the self-supervised learning paradigm for dynamic graph learning has not been sufficiently investigated due to the complicated properties of the evolving graphs. We assume that the dynamic graphs consist of three independent types of key factors, i.e., graph time-variant information, time-invariant information and noise. Based on this assumption, we propose the Self-supervised Decoupling for Dynamic Graph (SDDG) framework for explicitly decoupling the representation which characterizes these three factors, thus enhancing the interpretability of the learned representation and link prediction performance. More specifically, we design an encoder-decoder architecture to sufficiently exploit the dynamic graph itself with multiple regularizations, so that the time-variant embedding of dynamic graph data can be effectively decoupled from the perspectives of node and structure for time-variant-aware link prediction. Experiments on five benchmark link prediction tasks demonstrate the significant improvement of our SDDG over the state-of-the-art methods. For example, SDDG achieves 98.2% and 97.3% Top-1 AUC on Reddit-B and Reddit-T, outperforming the DDGCL model, by 1.9% and 1.8%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Supervised learning

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csl-MTFL: Multi-task Feature Learning with Joint Correlation Structure Learning for Alzheimer’s Disease Cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. They involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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Reinforcement Learning for Dynamic Mutation Process Control in Multi-Objective Differential Evolution

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IFAC-PapersOnLine 2022年第15期55卷 117-122页

作者： Lue Tao Gongshu Wang Yang Yang Yun Dong Lijie Su Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University China

To improve the search performance of the multi-objective differential evolution algorithm, we use a reinforcement learning agent to control the dynamic mutation process. First, a novel multi-objective optimization framework with new mutation operators is developed. Then, a Q-learning algorithm is introduced to select mutation operators with different characteristics adaptively. Specifically, we design three feature functions to describe the state of the population observed by the agent. Based on the observed state, the agent dynamically selects the mutation operator in each generation, which strongly improved the searching ability. We compare the performance of the proposed method with two state-of-art algorithms on benchmark functions and find that the proposed algorithm has a better performance in multiple indicators. At the same time, the component validity analysis also shows the effectiveness of the Q-learning and the framework introduced in this paper. Finally, the learning process shows that the agent can achieve asymptotic convergence.

关键词： Reinforcement Learning Evolutionary Algorithm Multi-Objective Deferential Evolution Mutation Process Control Operator Selection

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Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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OSN Rumor Control Model Based on Community Immunization

OSN Rumor Control Model Based on Community Immunization

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Ming Dong Zhiqiang Sun Zhenhua Tan Bin Zhang School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper proposes a rumor control model based on community immunization. Based on the community division and the trust network inference algorithm, the model redefines the standard to measure the importance of nodes in the network. First, the model uses the Louvain clustering algorithm based on the Ochiai coefficient to discover the network community and then presents the trust network inference algorithm. By analyzing the key factors that affect trust transfer between nodes, the trust evaluation between unfamiliar nodes is inferred, and important nodes with a high degree of trust in the network community are calculated. Finally, combined with the characteristics of inner degree and outer degree centrality of nodes in the network community, five types of important nodes in the network are screened out. To avoid repeated selection of nodes, this paper identifies a group of key nodes in the network community for local immunization by means of deduplication and taking intersection, so as to realize effective control of rumors in the network.

关键词： Distributed processing Clustering algorithms Inference algorithms Standards Open data

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuaizheng Chen Chaolu Feng Wei Li Jinzhu Yang Dazhe Zhao School of Computer Science And Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Training Measurement Deep learning Image resolution Computational modeling Supervised learning Estimation Image restoration Computational efficiency Convolutional neural networks

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