PQISEM: BN's structure learning based on partial qualitative influences and SEM algorithm from missing data

Abstract

The structure learning of Bayesian Network (BN) is an important issue for probabilistic inference of BN. In this paper, for missing data, we have proposed a BN's structure learning algorithm by making full use of some partial qualitative influences and SEM algorithm. Specifically, firstly, we address the problem that how to modify BN's parameters based on partial qualitative influence knowledge, which makes these parameters meet the given qualitative constraint relationship. Then, based on qualitative influences, we give random search operators in hill climbing method, and then analyse the selection rule of the initial network and selection strategy of candidate networks. Further, the PQISEM algorithm is proposed based on partial qualitative influences and SEM algorithm. Its complexity and convergence are analysed. Finally, the experiment illustrates PQISEM's performance by comparing with other algorithms on standard networks and discussing on different sample sizes and different missing value proportion.