针对高维数据下贝叶斯网络结构学习精度和效率低的问题,提出一种基于归一化互信息和近似马尔可夫毯的特征选择(feature selection based on normalized mutual information and approximate Markov blanket,FSNMB)算法来获取目标节点的马尔可夫毯(Markov blanket,MB),进一步结合 MB和 Meek规则实现基于特征选择的局部贝叶斯网络结构(construct local Bayesian network based on feature selection,FSCLBN)算法,提高局部贝叶斯网络结构学习的精度和效率.实验证明,在高维数据中,FSCLBN算法与现存的局部贝叶斯网络结构学习算法相比更具优势.
Local Bayesian network structure learning for high-dimensional data
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.
NETL
NSTL
万方数据
