Bayesian Network Structure Learning Algorithm Based on Node Order Constraint
Xiaoqing Li, Haizheng Yu · 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI) · 2022
The K2 algorithm is one of the classical algorithms for Bayesian Network structure learning. However, the learning effect of K2 algorithm strongly depends on the maximum node in-degree$\mu$and the node order$\rho$. In order to solve this problem, this paper proposes a new Bayesian Network structure learning algorithm: MI-Kruskal-K2 algorithm. Firstly, the algorithm calculates the mutual information MI between variables, and uses the Kruskal algorithm in Graph Theory to construct the maximum spanning tree to obtain the maximum node in-degree$\mu$; then, the maximum spanning tree was searched by Depth First Search to obtain the node order$\rho$; finally, the K2 algorithm calls the node in-degree$\mu$and the node order$\rho$to learn and obtain the optimal Bayesian Network structure. Experiments are carried out in a small Asia Network. Compared with the Greedy Search (GS) algorithm and Hill-Climbing (HC) algorithm, the MI-Kruskal-K2 algorithm performs better.