Structural Learning Bayesian Network Equivalence Classes Based on a Hybrid Method
Youlong Yang · Dianzi xuebao · 2013
Bayesian Network(BN)is one of the most important methods for representing and inferring with uncertainty knowledge,and also a powerful theory model within the community of artificial intelligence.To solve the drawbacks of hybrid methods for learning BNs which are easy to fall into local optimum and unreliable for learning large data set,we propose a novel hybrid algorithm for learning BN equivalence classes which combines ideas from maximal prime decomposition(MPD)of graph theory,conditional independence(CI)tests,and local search-and-score techniques in an effective way.It first reconstructs the undirected independence graph of a BN and then performs MPD to transform the undirected graph into its subgraphs.Finally,the new algorithm uses only lower-order CI tests and local BDeu score to check the v-structure of each subgraph.Theoretical and experimental results show that the proposed algorithm is correctness and effective.