Title: PQISEM: BN's structure learning based on partial qualitative influences and SEM algorithm from missing data
Authors: Yali Lv; Jian'ai Wu; Tong Jing
Addresses: School of Information Management, Shanxi University of Finance & Economics, Taiyuan 030006, China; Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China ' School of Information Management, Shanxi University of Finance & Economics, Taiyuan 030006, China ' School of Information Management, Shanxi University of Finance & Economics, Taiyuan 030006, China
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.
Keywords: probabilistic inference; Bayesian network; qualitative knowledge; missing data; qualitative probabilistic networks; partial qualitative influences; structure learning; parameter learning; SEM algorithm; hill climbing method.
International Journal of Wireless and Mobile Computing, 2018 Vol.14 No.4, pp.348 - 357
Received: 13 Feb 2018
Accepted: 08 Apr 2018
Published online: 28 Jul 2018 *