@inproceedings{059eade110cb4d7c9e88456e72573b7e,
title = "Bi-Objective Search Method for Bayesian Network Structure Learning",
abstract = "Bayesian network (BN) is a probability graph model, which makes uncertain reasoning logically clearer and more understandable. Structure learning is the first step to learn a BN model. And the score + search methods are a kind of the effective methods to learn the structure. This paper proposes a Bi-Objective Search (BOS) method for Bayesian network structure learning, which considers two objectives, i.e., the log-likelihood score and network complexity. To avoid the illegal structures, BOS samples edges and generates permutations to add directions to the edges for the initial population. To improve the diversity, BOS designs the genetic operators to generate new solutions. The new approach is applied to a set of discrete Bayesian networks, and the experimental results show that the algorithm is superior to the existing algorithms in BN structure learning.",
keywords = "Bayesian Network, Multi-objective optimization, Structure learning",
author = "Ting Wu and Hong Qian and Aimin Zhou and Zhenzi Li",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021 ; Conference date: 07-11-2021 Through 08-11-2021",
year = "2021",
doi = "10.1109/CCIS53392.2021.9754657",
language = "英语",
series = "Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "433--437",
editor = "Deyi Li and Mengqi Zhou and Weining Wang and Yaru Zou and Meng Luo and Qian Zhang",
booktitle = "Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021",
address = "美国",
}