贝叶斯网络结构学习的双重K2算法

贝叶斯网络源于人们对人工智能领域不确定性问题的研究，是进行不确定问题推理和数据分析的重要工具。结构学习是贝叶斯网络研究的核心内容，K2算法是结构学习的经典算法之一。为解决K2算法学习效果强烈依赖于节点序的问题，本文提出一种新的混合结构学习算法：双重K2算法。该算法首先将节点信息作为初始节点序，通过K2算法的搜索策略得到初始网络结构；然后在初始网络结构上利用拓扑排序得到修正后的节点序；最后K2算法通过修正后的节点序学习得到最优的网络结构。通过实验验证，在精度和效率上，双重K2算法效果优于其它经典算法。

Double K2 Algorithm for Bayesian Network Structure Learning

Bayesian network originates from people''s research on uncertainty problems in the field of artificial intelligence, and it is an important tool for uncertainty problems inference and data analysis. Structure learning is the core content of Bayesian network research, and the K2 algorithm is one of the classical algorithms for structure learning. In order to solve the problem that the learning effect of K2 algorithm strongly depends on the node order, this paper proposes a new hybrid structure learning algorithm: double K2 algorithm. The algorithm firstly takes the node information as the initial node order, and obtains the initial network structure through the search strategy of K2 algorithm. Then, the modified node order is obtained by using topological ordering on the initial network structure. Finally, K2 algorithm obtains the optimal network structure through the modified node order. Experimental results show that the double K2 algorithm is better than other classical algorithms in accuracy and efficiency.