Structural Learning Bayesian Network Based on a Hybrid Method
Ya Zhang · Electronic Science and Technology · 2014
A hybrid algorithm for structure learning of Bayesian network which based on maximal prime decomposition technology and genetic algorithm is proposed. The algorithm first constructs the undirected independence graph of a BN according to domain knowledge and observation data. Then it performs MPD to decompose the undirected graphs. The genetic algorithm is used to learn the local structure and combine the subgraphs then correct them to obtain the final BN. The decomposition splits the problem of learning a large network into some problems of learning small subgraphs. Experimental results show that the learning ability and performance of novel algorithm are improved significantly.