Structure learning algorithm for Bayesian network based on probability density kernel estimation
Han Shaoji · Computer Engineering and Applications Journal · 2014
Structure learning algorithms for a Bayesian network mainly include hill-climbing algorithm, K2 algorithm and so on. However, these algorithms require large sample data sets. For the small sample sets in practical problems, this paper introduces the probability density kernel estimation method to achieve the expansion of the original sample set, and then uses the K2 algorithm for a Bayesian network structure learning. By optimizing the kernel function and window width, it achieves the effective expansion of the original sample set based on probability density kernel estimation; it confirms the variable order based on mutual information, and then establishes a Bayesian structure learning algorithm based on a small sample set. Simulation results show that the algorithm is effective and practical.