Bayesian Network Structure Learning by Ensemble Learning and Frequent Item Mining

Abstract

Aiming at the common problem of low learning effect in single structure learning of a Bayesian network, a new algorithm EF-BNSL integrating ensemble learning and frequent item mining is proposed. Firstly, the sample set is obtained by sampling the original dataset using Bootstrap, which is mined using the Apriori algorithm to derive the maximum frequent items and association rules so that the black and white list can be determined. Secondly, considering that there may be wrong edges in the black and white list, the black and white list is used as the penalty term of the BDeu score and the initial network is obtained from the hill climbing algorithm. Finally, repeat the above steps 10 times to obtain 10 initial networks. The 10 initial networks were integrated and learned by the integrated strategy function to obtain the final Bayesian network. Experiments were carried out on six standard networks to calculate $F_1$ score and $\mathrm{H}\mathrm{D}$ . The results show that the EF-BNSL algorithm can effectively improve $F_1$ score, reduce $\mathrm{H}\mathrm{D}$ , and learn the network structure that is closer to the real network.