Bayesian Network Structure Algorithm Based on V-structure & Log-Likelihood Orientation and Tabu Hill Climbing

Hill climbing algorithm has too large search space and is easy to fall into local optimum. In this paper, a new Bayesian network structure algorithm based on V-structure & log-likelihood orientation and Tabu Hill (VTH) climbing is proposed. The algorithm limits the search space by using the oriented maximum weight spanning tree. In the process of maximum weight spanning tree orientation, the orientation strategy based on V-structure and Log-Likelihood (VLL) function is proposed. Tabu Hill Climbing (THC) scoring search strategy is established during the process of search, it combines the tabu list clearing mechanism with the local optimization criteria of hill climbing, the strategy not only ensures the search efficiency, but also improves the global optimization ability. By comparing Hamming distance, F1-value, Balanced Scoring Function(BSF) value and Time with other algorithms in Asia, Car, Child and Alarm standard networks, the effectiveness of the proposed algorithm is verified.