A Score Based Approach towards Improving Bayesian Network Structure Learning

Abstract

In big data research, an important field is the big data graph algorithm. The Bayesian Network (BN) is a very powerful graph model for causal relationship modeling and probabilistic reasoning. One key process of building a BN is discovering its structure -- a directed acyclic graph (DAG). In the literature, numerous Bayesian network structure learning algorithms are proposed to discover BN structure from data. However, facing structures learned by different learning algorithms, a general purpose improvement algorithm is lacking. This study proposes a novel algorithm called SBNR (Score-based Bayesian Network Refinement). SBNR leverages Bayesian score function to enrich and rectify BN structures. Empirical study applies SBNR to BN structures learned by three major BN learning algorithms: PC, TPDA and MMHC. Up to 50% improvements are observed, confirming the effectiveness of SBNR towards improving BN structure learning. SBNR is a general purpose algorithm applicable to different BN learning with small computational overhead. Therefore, SBNR can be helpful to advance big data graphic model learning.