1 citations · 1 across the 11 of their papers we have counts for
12 papers
Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification
Wenbin Pei, Yunrong Hao, Zhen Liu +4
Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced da…
HPSO: Particle Swarm Optimization with Hypergraph-Based Topology
Wenbin Pei, Xi Luo, Bing Xue +2
Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-world applications due to its efficient exploration of large solution spa…
Enhancing Discrete Particle Swarm Optimization for Hypergraph-Modeled Influence Maximization
Qianshi Wang, Xilong Qu, Wenbin Pei +2
Influence maximization (IM) is a fundamental problem in complex network analysis, with a wide range of real-world applications. To date, existing approaches to influential node ide…
Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
Wenbin Pei, Ruohao Dai, Bing Xue +3
Time series classification is a fundamental machine learning task with broad real-world applications. Although many deep learning methods have proven effective in learning time-ser…
HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness
Chengyu Tian, Wenbin Pei
Robustness in complex systems is of significant engineering and economic importance. However, conventional attack-based a posteriori robustness assessments incur prohibitive comput…
Meta-heuristic Hypergraph-Assisted Robustness Optimization for Higher-order Complex Systems
Xilong Qu, Wenbin Pei, Haifang Li +3
In complex systems (e.g., communication, transportation, and biological networks), high robustness ensures sustained functionality and stability even when resisting attacks. Howeve…
Federated Unlearning Model Recovery in Data with Skewed Label Distributions
Xinrui Yu, Wenbin Pei, Bing Xue +1
In federated learning, federated unlearning is a technique that provides clients with a rollback mechanism that allows them to withdraw their data contribution without training fro…
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning
Wenbin Pei, Ruohao Dai, Bing Xue +4
Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class…
UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba
Li Wu, Wenbin Pei, Jiulong Jiao +1
Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art meth…
DivDiff: A Conditional Diffusion Model for Diverse Human Motion Prediction
Hua Yu, Yaqing Hou, Wenbin Pei +1
Diverse human motion prediction (HMP) aims to predict multiple plausible future motions given an observed human motion sequence. It is a challenging task due to the diversity of po…
Influence Maximization in Hypergraphs Using A Genetic Algorithm with New Initialization and Evaluation Methods
Xilong Qu, Wenbin Pei, Yingchao Yang +3
Influence maximization (IM) is a crucial optimization task related to analyzing complex networks in the real world, such as social networks, disease propagation networks, and marke…
Improving Critical Node Detection Using Neural Network-based Initialization in a Genetic Algorithm
Chanjuan Liu, Shike Ge, Zhihan Chen +4
The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network. These nodes play a significant role in maintaining the connectivity of the ne…