@inproceedings{750d13ab493b46fb9d7fe533a3f156bf,
title = "Local Bayesian Network Structure Learning for High-Dimensional Data",
abstract = "To address the challenge of achieving higher learning accuracy and efficiency in local Bayesian network structure learning for high-dimensional data, we introduce a new algorithm combines feature selection with the Meek rules to construct local Bayesian network structures, known as FSCLBN. Our experimental results show that the average F1 scores of FSCLBN and the other three algorithms (PCD\_by\_PCD, CMB, MY\_by\_MB) on all data sets are 0.56, 0.37, 0.44 and 0.21 respectively. Therefore, the FSCLBN algorithm outperforms traditional local Bayesian network structure learning methods when dealing with high-dimensional data sets.",
keywords = "Bayesian network, feature selection, Markov blanket, mutual information",
author = "Yangyang Wang and Xiaoguang Gao and Pengzhan Sun and Xinxin Ru and Jihan Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 9th International Conference on Control and Robotics Engineering, ICCRE 2024 ; Conference date: 10-05-2024 Through 12-05-2024",
year = "2024",
doi = "10.1109/ICCRE61448.2024.10589754",
language = "英语",
series = "2024 9th International Conference on Control and Robotics Engineering, ICCRE 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "259--263",
booktitle = "2024 9th International Conference on Control and Robotics Engineering, ICCRE 2024",
}