Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers on the output model. (2) An extra stabilizer term was added to each objective function. As a result, the learning rules of the proposed method are stable. (3) To reduce the computational cost of finding KNNs for all the samples, location difference of multiple distances based k-nearest neighbors algorithm (LDMDBA) was embedded into the learning process of the proposed method. The extensive experimental results on several synthetic and benchmark datasets show the effectiveness of our proposed RKNN-TSVM in both classification accuracy and computational time. Moreover, the largest speedup in the proposed method reaches to 14 times.
Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
Missouri University of Science and Technology（密苏里科技大学）；Grainger College of Engineering（格兰杰工程学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National Center for Supercomputing Applications（国家超级计算应用中心）
Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
State Key Laboratory of ISN（ISN国家重点实验室）；School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Cyber Engineering, Xidian University（西安电子科技大学网络与信息安全学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks
National Mobile Communications Research Lab, Southeast University, Nanjing 210096, China（南京东南大学国家移动通信研究中心）；Pervasive Communication Research Center, Purple Mountain Laboratories, Nanjing 211111, China（南京紫金山实验室 pervasive 通信研究中心）
Enhanced Predictive Modeling for Hazardous Near-Earth Object Detection: A Comparative Analysis of Advanced Resampling Strategies and Machine Learning Algorithms in Planetary Risk Assessment
Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production
Karlstad University, Computer Science Department（卡尔斯塔德大学计算机科学系）；University of Bologna, Department of Computer Science and Engineering（博洛尼亚大学计算机科学与工程系）；Deggendorf Institute of Technology, Faculty of Applied Computer Science（德格多夫技术学院应用计算机科学系）；Smart Industrial Automation, RISE Research Institutes of Sweden（智能工业自动化，瑞典RISE研究机构）
