Bayesian network structure learning algorithm based on maximal information coefficient
Wei Zhong-qian · Jisuanji yingyong yanjiu · 2014
In order to obtain the correct node ordering,this paper presented a new Bayesian network structure learning algorithm(MICVO) which used the method based on maximal information coefficient combining with conditional independence test. Firstly,it generated an initial undirected graph through measuring dependency between variables using maximal information coefficient,and introduced a penalty factor δ to reduce the number of redundant edges. Then divided this undirected graph into multiple sub-structures to determine the direction of edges in the graph,and finally the initial ordering of nodes obtained was as input of K2 algorithm to construct the network structure. Experimental results over two benchmark networks Asia and Alarm prove that the Bayesian network structure learning algorithm based on maximal information coefficient can obtain bear optimal ordering of nodes,network structure with better degree of data matching,and higher classification accuracy.