A new approach to learn the equivalence class of bayesian network

作者：Zhang Ying Xia^*; Yang You Long; Cui Jian Fei

来源：Journal of Dong Hua University (english Edition), 2015, 32(2): 257-261.

• Causal relations
• Conditional independences
• Constraint-based
• Equivalence class of bayesian networks
• Learning Bayesian networks
• New approaches
• Structure-learning
• Two phase method

It's a well-known fact that constraint-based algorithms for learning Bayesian network (BN) structure reckon on a large number of conditional independence (CI) tests. Therefore, it is difficult to learn a BN for indicating the original causal relations in the true graph. In this paper, a two-phase method for learning equivalence class of BN is introduced. The first phase of the method learns a skeleton of the BN by CI tests. In this way, it reduces the number of tests compared with other existing algorithms and decreases the running time drastically. The second phase of the method orients edges that exist in all BN equivalence classes. Our method is tested on the ALARM network and experimental results show that our approach outperforms the other algorithms.

• 出版日期2015
• 单位西安电子科技大学