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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Superpoints Guided Local Explanation For Deep 3D Trackers

Superpoints Guided Local Explanation For Deep 3D Trackers

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cheng, Riran Wang, Xupeng Sohel, Ferdous Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Information Technology Murdoch University Murdoch Australia

ISBN: (纸本)9798350368741

3D object tracking has become a popular research topic because of its broad application prospects. However, it remains a challenging task to advance the trustworthiness of deep trackers, caused by the complex network structure of black-box models. In this paper, a local explanation method for 3D object tracking is proposed, which trains an interpretable surrogate model to reveal the contribution of superpoints in the search area. Specifically, local points of the search area with comparable geometric features are aggregated as superpoints, which serve as the fundamentals of the explanation. In contrast to the commonly used voxels, superpoints capture semantic information of the search area, and facilitate an intuitive understanding of the predictions. In addition, a distance-aware masking strategy is proposed for generating the sample set to train a surrogate model, which corresponds to latent contributions of superpoints to predictions and improves the efficacy of the explanation. Experiments have demonstrated that the proposed explainability approach can effectively provide explanations to deep tracking models. © 2025 IEEE.

关键词： 3D object tracking local explainability method surrogate model

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Resilient multi-objective mission planning for UAV formation:A unified framework integrating task pre-and re-assignment

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Defence technology(防务技术) 2025年第3期45卷 203-226页

作者： Xinwei Wang Xiaohua Gao Lei Wang Xichao Su Junhong Jin Xuanbo Liu Zhilong Deng Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalianLiaoning 116024China School of Mathematics and Big Data Anhui University of Science and TechnologyHuainanAnhui 232001China School of Mathematical Science Dalian University of TechnologyDalianLiaoning 116024China Department of Airborne Vehicle Engineering Naval Aeronautical and Astronautical UniversityYantai 264001China

Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.

关键词： Cooperative mission planning UAV formation Mission reliability Evolutionary algorithm Contract net protocol

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Exploration and Practice of Deep Learning Classroom Teaching of 'Computer Network' Course Based on the Integration of Specialization and Innovation 5

Exploration and Practice of Deep Learning Classroom Teaching...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Huang, Min Peng, ShaoWu Yuan, Hua School of Software Engineering South China University of Technology Guangzhou China School of Computer Science South China University of Technology Guangzhou China

ISBN: (纸本)9798331528409

In the era of rapid development of artificial intelligence technologies, traditional teaching models are unable to meet the employment needs of enterprises, and talent cultivation in universities faces more challenges, so teachers should consider how to better cultivate students' innovative abilities in classroom teaching and guiding them in practical applications. The paper focuses on teaching reform and practice of 'Computer Networks' course by deep learning classroom, which mainly includes three aspects: 1) Integrating ideological and political education and innovative education into the process of professional education of in the curriculum, by which to enhance students' professional innovation awareness;2) Inviting experts from enterprises to participate in classroom teaching and practical guidance to help students learn about the ability requirements and related positions in the IT enterprises for talents in the era of AI;3) Designing practice projects and guiding students to implement them by 'design thinking' methods, and also adopting an evaluation method that can reflect the innovative ability of students. Works of the paper can better reflect the support and deepening the teaching philosophy of studentcentered, and provide an attempt to improve teaching effectiveness and talent cultivation quality. © 2025 IEEE.

关键词： Cultivation

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Optimizing Intrusion Detection with Hybrid Deep Learning Models and Data Balancing Techniques 4

Optimizing Intrusion Detection with Hybrid Deep Learning Mod...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Sarwar, Md. Nasif Arman, Md. Shohel Bhuiyan, Touhid Rafiq, Fatama Binta Daffodil International University Department of Software Engineering Dhaka Bangladesh Washington University of Science and Technology School of Information Technology Virginia United States

ISBN: (纸本)9798331518882

The effectiveness of hybrid deep learning models in detecting network intrusions on imbalanced datasets was tested. Conventional IDS often misses rare attacks due to class imbalance. Three models were evaluated: CNN+DNN, Autoencoder+DNN, and DNN+XGBoost, using the CSC IDS 2018 dataset balanced with SMOTE, FSMOTE, and CTGAN techniques. Feature selection was performed using SHAP and correlation studies. The models were evaluated on accuracy, precision, recall, F1-score, and ROC AUC. The Autoencoder+DNN and CNN+DNN models, especially with FSMOTE and SMOTE, showed the best performance. This research highlights the potential of combining advanced feature selection, data balancing, and deep learning techniques to enhance network intrusion detection, offering a robust framework for improved cybersecurity strategies. © 2025 IEEE.

关键词： Network intrusion

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Dual-encoder model for typhoon path prediction with multiscale spatiotemporal data fusion

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Earth Science Informatics 2025年第2期18卷 1-17页

作者： Ren, Shuxia Zhong, Ruikun Guo, Zewei Zhang, Zining School of Software Engineering Tiangong University Tianjin 300387 China School of Computer Science and Technology Tiangong University Tianjin 300387 China

Extreme weather caused by typhoons poses a severe threat to human life safety and socio-economic development, making accurate prediction of typhoon paths crucial. However, existing prediction models struggle to effectively handle and integrate heterogeneous data, overlooking deep correlations between the data, which in turn affects the accuracy of the models. This paper proposes a Dual-Encoder Spatiotemporal Fusion Model for Typhoon Path Prediction (DESF-Typhoon), designed to effectively integrate data from different time scales and extract the underlying relationships between the data, thereby improving prediction accuracy. Specifically, we designed a dual-encoder module that effectively captures complex nonlinear data structures, accounting for the intricate influences of factors such as geography and environment on path, while integrating spatial features of typhoon at different time scales. Subsequently, we introduced a feature interaction module to explore the relationships between different features, which can adaptively learn the interaction weights between them, resulting in a richer feature representation. We evaluated the model using the dataset from the China Meteorological Administration (CMA) and compared its performance with traditional prediction methods and deep learning-based approaches. The results demonstrate that the model significantly improves accuracy and robustness, making innovative contributions in data fusion and spatiotemporal feature modeling. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Dual encoder Feature interaction Spatiotemporal data Tropical cyclone prediction

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RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2132-2145页

作者： Lin, Dan Wu, Jiajing Fu, Qishuang Zheng, Zibin Chen, Ting Sun Yat-sen University School of Software Engineering Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610051 China

As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals. Differently from traditional financial scenarios, executing Know Your Customer verification on Ethereum is rather difficult due to its pseudonymous nature. Fortunately, as the transaction records stored in the Ethereum blockchain are publicly accessible, we can understand the behavior of accounts or detect illicit activities via transaction mining. Existing risk control techniques have primarily been developed from the perspectives of de-anonymizing address clustering and illicit account classification. However, these techniques cannot ascertain the potential risks for all accounts and are limited by specific heuristic strategies or insufficient label information. These constraints motivate us to seek an effective rating method for quantifying the spread of risk in a transaction network. To the best of our knowledge, we are the first to address the problem of account risk rating on Ethereum by proposing a novel model called RiskProp, which includes a de-anonymous score to measure transaction anonymity and a network propagation mechanism to formulate the relationships between accounts and transactions. Experimental results on a realistic Ethereum dataset demonstrate that proposed RiskProp newly discovered 63% of the Top 150 high-risk accounts as suspicious. The superior performance of risk score-based account classification experiments further verifies the effectiveness of our rating method (85.63% accuracy). © 2024 IEEE.

关键词： Ethereum

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Automata Learning for React Web Applications 18

Automata Learning for React Web Applications

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18th IEEE International Conference on software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Grubelnik, Peter Wotawa, Franz Graz University of Technology Graz Austria Graz University of Technology Institute of Software Engineering and Artificial Intelligence Graz Austria

ISBN: (纸本)9798331534677

Testing is an inevitable part of any software engineering process to ensure quality and reliability. Model-based testing is a successful approach for the automated generation of test cases but requires a model of the system under test. Constructing such models can take time and effort. Hence, there is even a need to automate model generation, where automata learning is a promising approach. In this paper, we address the application of automata learning for React web forms used in a wide range of web applications. We provide information regarding a tool we developed to couple web forms to automata learning for obtaining a model. In addition, we discuss the application of the tool, limitations, challenges, and solutions for reducing the state space and making the approach feasible for practical applications. © 2025 IEEE.

关键词： software testing

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning 31

An Efficient Dialogue Policy Agent with Model-Based Causal R...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Kai Wang, Zhenyu Zhao, Yangyang Fang, Bopeng School of Software Engineering South China University of Technology Guangdong China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798891761964

Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviate the sample inefficiency. However, traditional world model methods merely consider one-step dialogues, leading to an inaccurate environmental simulation. Furthermore, different users may have different intention preferences, while most existing studies lack consideration of the intention-preferences causal relationship. This paper proposes a novel framework for dialogue policy learning named MCA, implemented through model-based reinforcement learning with automatically constructed causal chains. The MCA model utilizes an autoregressive Transformer to model dialogue trajectories, enabling a more accurate simulation of the environment. Additionally, it constructs a causal chains module that outputs latent preference distributions for intention-action pairs, thereby elucidating the relationship between user intentions and agent actions. The experimental results show that MCA can achieve state-of-the-art performances on three dialogue datasets over the compared dialogue agents, highlighting its effectiveness and robustness. © 2025 Association for Computational Linguistics.

关键词： Deep reinforcement learning

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