E-GSA algorithm for learning Bayesian network with missing data
Yang Zhang · Jisuanji gongcheng yu sheji · 2013
For solving the difficulty of building Bayesian network with missing data,structural learning algorithm of Bayesian network is studied,an algorithm combines conditional independence test and score-search is presented.First,the initial network is built by the initialization of the training data using improved hybrid algorithm.Then make use of genetic-simulated annealing algorithm to train the initial network which has combined the training data in order to find the best network.Detailed operation steps are given out and the algorithm is also compared to other well-known algorithms.Experimental results indicate that this algorithm makes a more effective study performance than several other algorithms.