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A novel hybrid butterfly optimization algorithm for feature selection with sine cosine velocity in the high-dimensional classification data

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 369-391页

作者： Zhang, Li Chen, Xiaobo Key Laboratory of Data Science and Intelligence Education Hainan Normal University Ministry of Education Haikou Hainan China School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Changzhou City Center Branch People's Bank of China Changzhou Jiangsu China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm's global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, individual butterflies' autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm's effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. © 2024 - IOS Press. All rights reserved.

关键词： Optimization algorithms

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AAV-Assisted Joint Mobile Edge computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第12期12卷 19782-19800页

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Applying Kumaraswamy distribution on stick-breaking process: a Dirichlet neural topic model approach

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Neural computing and Applications 2024年第22期36卷 13731-13744页

作者： Ouyang, Jihong Wang, Teng Cao, Jingyue Wang, Yiming College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130015 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130015 China

In recent years, neural topic modeling has increasingly raised extensive attention due to its capacity on generating coherent topics and flexible deep neural structures. However, the widely used Dirichlet distribution in shallow topic models is difficult to reparameterize. Therefore, most existing neural topic models assume the Gaussian as the prior of topic proportions for reparameterization. Gaussian distribution does not have the sparsity like Dirichlet distribution, which limits the model’s topic extraction ability. To address this issue, we propose a novel neural topic model approximating the Dirichlet prior with the reparameterizable Kumaraswamy distribution, namely Kumaraswamy Neural Topic Model (KNTM). Specifically, we adopted the stick-breaking process for posterior inference with the Kumaraswamy distribution as the base distribution. Besides, to capture the dependencies among topics, we propose a Kumaraswamy Recurrent Neural Topic Model (KRNTM) based on the recurrent stick-breaking construction to ensure that the model can still generate coherent topical words in high-dimensional topic space. We examined our method on five prevalent benchmark datasets over six Dirichlet-approximating neural topic models, among which KNTM has the lowest perplexity and KRNTM performance best on topic coherence and topic uniqueness. Qualitative analysis of the top topical words verifies that our proposed models can extract more semantically coherent topics compared with state-of-the-art models, further demonstrating our method’s effectiveness. This work contributes to the broader application of VAEs with Dirichlet priors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Benchmarking

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Caption-Aware Multimodal Relation Extraction with Mutual Information Maximization 24

Caption-Aware Multimodal Relation Extraction with Mutual Inf...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zefan Zhang, Weiqi Li, Yanhui Bai, Tian College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

ISBN: (纸本)9798400706868

Multimodal Relation Extraction (MRE) has achieved great improvements. However, modern MRE models are easily affected by irrelevant objects during multimodal alignment which are called error sensitivity issues. The main reason is that visual features are not fully aligned with textual features and the reasoning process may suppress redundant and noisy information at the risk of losing critical information. In light of this, we propose a Caption-Aware Multimodal Relation Extraction Network with Mutual Information Maximization (CAMIM). Specifically, we first generate detailed image captions through the Large Language Model (LLM). Then, the Caption-Aware Module (CAM) hierarchically aligns the fine-grained visual entities and textual entities for reasoning. In addition, for preserving crucial information within different modalities, we leverage a Mutual Information Maximization method to regulate the multimodal reasoning module. Experiments show that our model outperforms the state-of-the-art MRE models on the benchmark dataset MNRE. Further ablation studies prove the pluggable and effective performance of our Caption-Aware Module and Mutual Information Maximization method. Our code is available at https://***/zefanZhang-cn/CAMIM. © 2024 ACM.

关键词： Modeling languages

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Harmfully Manipulated Images Matter in Multimodal Misinformation Detection 24

Harmfully Manipulated Images Matter in Multimodal Misinforma...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Bing Wang, Shengsheng Li, Changchun Guan, Renchu Li, Ximing College of Computer Science and Technology Jilin University Jilin Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin University China

ISBN: (纸本)9798400706868

Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying misinformation, especially those containing multimodal content, has attracted growing attention from the academic and industrial communities, and induced an active research topic named Multimodal Misinformation Detection (MMD). Typically, existing MMD methods capture the semantic correlation and inconsistency between multiple modalities, but neglect some potential clues in multimodal content. Recent studies suggest that manipulated traces of the images in articles are non-trivial clues for detecting misinformation. Meanwhile, we find that the underlying intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Accordingly, in this work, we propose to detect misinformation by learning manipulation features that indicate whether the image has been manipulated, as well as intention features regarding the harmful and harmless intentions of the manipulation. Unfortunately, the manipulation and intention labels that make these features discriminative are unknown. To overcome the problem, we propose two weakly supervised signals as alternatives by introducing additional datasets on image manipulation detection and formulating two classification tasks as positive and unlabeled learning problems. Based on these ideas, we propose a novel MMD method, namely Harmfully Manipulated Images Matter in MMD (Hami-m3d). Extensive experiments across three benchmark datasets can demonstrate that Hami-m3d can consistently improve the performance of any MMD baselines. © 2024 ACM.

关键词： image manipulation

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An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 734-754页

作者： Yong Zhang Ying Hu Xiaozhi Gao Dunwei Gong Yinan Guo Kaizhou Gao Wanqiu Zhang School of Information and Control Engineering China University of Mining and TechnologyXuzhouChina The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina School of Computing University of Eastern FinlandKuopioFinland The Macao Institute of Systems Engineering Macao University of Science and TechnologyTaipaChina

In real life,a large amount of data describing the same learning task may be stored in different institutions(called participants),and these data cannot be shared among par-ticipants due to privacy *** case that different attributes/features of the same instance are stored in different institutions is called vertically distributed *** pur-pose of vertical‐federated feature selection(FS)is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature *** solve this problem,in the paper,an embedded vertical‐federated FS algorithm based on particle swarm optimisation(PSO‐EVFFS)is proposed by incorporating evolutionary FS into the SecureBoost framework for the first *** optimising both hyper‐parameters of the XGBoost model and feature subsets,PSO‐EVFFS can obtain a feature subset,which makes the XGBoost model more *** the same time,since different participants only share insensitive parameters such as model loss function,PSO‐EVFFS can effec-tively ensure the privacy of participants'***,an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each ***,the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation ***-mental results show that the proposed algorithm can significantly improve the classifi-cation performance of selected feature subsets while fully protecting the data privacy of all participants.

关键词： Evolutionary optimization feature selection privacy protection vertical‐federated learning

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WST: Wavelet-Based Multi-scale Tuning for Visual Transfer Learning 39

WST: Wavelet-Based Multi-scale Tuning for Visual Transfer Le...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zeng, Jia Huang, Lan Wang, Kangping College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)157735897X

Large-scale pre-trained Vision Transformer (ViT) models have demonstrated remarkable performance on visual tasks but are computationally expensive to transfer to downstream tasks. Parameter-Efficient Fine-Tuning (PEFT) offers a promising transferring approach by updating only a subset of parameters. However, PEFT’s effectiveness is hindered by discrepancies between pre-training and downstream tasks in terms of object scale and granularity. Downstream tasks often focus on finer-grained and more specialized recognition, requiring more detailed features. The diversity of feature scales of existing PEFT methods for ViT is limited. To address this, we propose a novel PEFT method named Wavelet-based multi-Scale Tuning (WST), which learns multi-scale features in a simple and efficient way. WST introduces a parallel finetuning patch embedding branch with a smaller patch size than the pre-trained model to capture finer-grained features. Furthermore, to handle the computational challenge from the resulting longer token sequence, WST designs wavelet finetuning blocks that balance both efficiency and performance. In the block, wavelet transform enables invertible and lossless down-sampling of the longer token sequence, aligning it with that of the backbone, and two lightweight linear mappings are employed to learn task-specific features. This design facilitates efficient multi-scale information exchange between the pre-trained backbone and fine-tuning branch. Extensive experiments on transfer learning demonstrate the promising performance and efficiency of our WST. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Transfer learning

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Contrastive Auxiliary Learning with Structure Transformation for Heterogeneous Graphs 39

Contrastive Auxiliary Learning with Structure Transformation...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Wei Sun, Hongmin Gao, Hang Li, Gaoyang Li, Ying Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University China School of Life Sciences and Technology Tongji University China

ISBN: (纸本)157735897X

In recent years, methods based on heterogeneous graph neural networks (HGNNs) have been widely used for embedding heterogeneous graphs (HGs) due to their ability to effectively encode the rich information from HGs into low-dimensional node embeddings. Existing HGNNs focus on neighbor aggregation and semantic fusion while neglecting the HG structure and learning paradigms. However, the original data in the HG might lack node features, which may not be effectively accounted for by existing models. Additionally, exclusively relying on a single supervised learning approach may only partially leverage the invariant information in graph data. To address these challenges, we introduce the Contrastive Auxiliary Learning Model for Heterogeneous Graphs (CALHG), which combines edge perturbation and graph diffusion to enhance graph data, allowing it to capture the inherent structural information within heterogeneous graphs fully. Additionally, we employ a category-guided multi-view contrastive learning optimizing strategy, which does not rely on positive and negative samples for model training, enabling us to capture the intrinsic invariances in heterogeneous graph data. Extensive experiments and analyses on five benchmark datasets without node features and three benchmark datasets with node features validate the effectiveness and efficiency of our novel method compared with several state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Utterance-level Emotion Recognition in Conversation with Conversation-level Supervision 39

Utterance-level Emotion Recognition in Conversation with Con...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Ximing Dai, Yuanchao Yang, Zhiyao Chi, Jinjin Gao, Wanfu Wu, Lin Yuanbo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Swansea University United Kingdom

ISBN: (纸本)157735897X

Emotion Recognition in Conversations (ERC) involves automatically identifying the emotion of each utterance in conversations. The emotion of an utterance is contingent to the conversation context, and thus, annotating each utterance in ERC entails repetitive screening the whole conversation from annotators. Such a requirement leads to prohibitive cost in fine-grained labeling on utterance. In this paper, we propose an efficient coarse-grained labeling strategy for ERC, which assigns a set of emotions for each conversation. In specific, we reformulate the ERC predictors with conversation-level emotion sets as weakly-supervised learning to optimise a potential candidate for ERC, which is termed as Dataless ERC (DERC). To validate this, we propose a simple-yet-flexible DERC framework with Progressive Learning (DERC-PL). We jointly update pseudo-utterance-level emotions and the ERC predictor in a self-training manner, where we progressively update the ERC predictor from training subsets with lower noise densities to the ones with higher noise densities. We implemented several versions of DERC-PL by incorporating various off-the-shelf ERC methods. Extensive experimental results demonstrate that the proposed DERC-PL can be on par with existing weakly-supervised learning baselines and supervised learning ERC methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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Few-Shot Fine-Grained Classification of Histological Images 1

Few-Shot Fine-Grained Classification of Histological Images

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Jiang, Yingdong Huang, Jing Jin, Zihe Shen, Leqi Zhang, Ziyi College of Software Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education ChangChun China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education ChangChun China College of Computer Science and Technology Jilin University ChangChun China

ISBN: (纸本)9798350358780

Histological image classification plays a crucial role in cancer diagnosis. However, the acquisition of well-labeled histological images is prohibitively expensive, and obtaining rare abnormal samples is challenging. Therefore, applying few-shot learning methods to histological image classification tasks holds significant clinical value. Nevertheless, existing research predom-inantly relies on coarse-grained image classification approaches based on natural image datasets, which struggle to address the fine-grained challenges encountered in histological image classification, such as intra-class diversity and inter-class similarity. To tackle this issue, this study proposes a novel few-shot fine-grained classification method for histological images, named 'Category-Aware Feature Map Reconstruction Network.' This method employs channel weights to localize the differences between inter-class and intra-class regions, composed of intra-class channel weights and inter-class channel weights, collectively referred to as category-aware weights. Specifically, intra-class channel weights indicate the matching degree of salient regions within the support set of a particular class, while inter-class channel weights represent the degree of containing distinct information between classes. The category-aware weights are utilized to transform the support feature maps and query feature maps, generating feature maps that capture differentiating details between categories. Finally, the distance between the transformed query feature map and support feature map is calculated to achieve probabilistic predictions for the categories. On a histological few-shot dataset, this method achieves an accuracy of 90.23% using ResNet-12 as the feature extractor, surpassing the baseline model by 5.24% and outperforming other few-shot methods by at least 10% in the 5-way 10-shot experimental setting. The proposed method exhibits exceptional performance on histological image few-shot datasets, playing a

关键词： Computer aided diagnosis

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