A Score Based Approach towards Improving Bayesian Network Structure Learning - Yan Tang; Zhuoming Xu (2014 Second International Conference on Advanced Cloud and Big Data, 2014)
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.