@inproceedings{c1db84c658c84bdfbbcece6d8b1a658e,
title = "The evolutionary learning method of Bayesian network structure based on expert knowledge",
abstract = "Bayesian Network, which has been widely used for its significant advantages in causal inference, has great potential in product design. However, it is difficult to learn a reasonable network structure facing the problem with small data during the design process. In order to solve this problem, this paper introduces expert knowledge and Genetic Algorithm based on matrix coding into the process of Bayesian Network structure learning. Integrating the expert knowledge and data is a decent way to make up for the problem of insufficient data, and Genetic Algorithm is used to improve traditional structure learning algorithms, so as to obtain a more suitable structure conforming to the knowledge and data characteristics. The solutions illustrate that the Genetic Algorithm has some advantages compared with traditional structure learning method, and the use of expert knowledge can improve the rationality of the learned structure.",
keywords = "Bayesian Network, Expert knowledge, Genetic Algorithm, Matrix coding",
author = "Jiahui Wang and Lei Zhao and Yan Yan and Jia Hao",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Conference on Biometrics, Microelectronic Sensors, and Artificial Intelligence, BMSAI 2022 ; Conference date: 25-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2640077",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Wei Wei and Yang Yue",
booktitle = "International Conference on Biometrics, Microelectronic Sensors, and Artificial Intelligence, BMSAI 2022",
address = "United States",
}