基于互信息的贝叶斯网络结构学习算法

Bayesian Network Structural Learning Algorithm Based on Mutual Information

贝叶斯网络结构学习是贝叶斯网络构建的核心,有效的结构学习算法是构建最优网络结构的基础。基于此,提出一种基于互信息的贝叶斯网络结构学习算法,该算法可以挖掘出数据集各属性中存在的隐含依赖关系,适时地对数据集进行降维操作,从而提高算法的效率,并可保证结果的准确性。实验结果表明,与常用的依赖分析算法SGS相比,在结果相似的情况下,该算法执行效率更高。

Bayesian network structural learning plays a very important role in the processing of Bayesian network＇s construction,and an effective structural learning algorithm is the base of constructing the optimum Bayesian network.An algorithm of Bayesian network structural learning（called MIBNS） based on mutual information is proposed.The algorithm can give the concealed dependency relationships among data attributes,and make dimension reduction at the right moment,which can improve the performed efficiency and ensure the accuracy rate.Experimental result shows that the algorithm is effective.Compared with the SGS,the algorithm of MIBNS is more effective in the similar results.