高维数据局部贝叶斯网络结构学习

To address the issue of low learning accuracy and efficiency of Bayesian network structure learning under high-dimensional data, a feature selection based on normalized mutual information and approximate Markov blanket (FSNMB) algorithm is proposed to obtain the Markov blanket (MB) of the target node. The MB and Meek's rule are further combined to implement the algorithm of construct local Bayesian network based on feature selection (FSCLBN), which improves the accuracy and efficiency of local Bayesian network structure learning. Experiment results show that in high-dimensional data, the FSCLBN algorithm has more advantages than the existing local Bayesian network structure learning algorithms.