Bayesian Network Structure Learning Based on Probability Density Kernel Estimation
Shao Jin Han, Jian Xun Li · Applied Mechanics and Materials · 2013
The traditional structure learning algorithms are mainly faced with a large sample dataset. But the sample dataset practically is small. Based on it, we introduce the Probability Density Kernel Estimation (PDKE), which would achieve the expansion of the original sample sets. Then, the K2 algorithm is used to learn the Bayesian network structure. By optimizing the kernel function and window width, PDKE achieves the effective expansion of the original dataset. After the confirm of variable order based on mutual information, a small sample set of Bayesian structure learning algorithm would be established. Finally, simulation results confirm that the new algorithm is effective and practical.