A Score Based Approach towards Improving Bayesian Network Structure Learning

• Published in: 2014 Second International Conference on Advanced Cloud and Big Data pp. 39 - 44
• Main Authors: Yan Tang, Zhuoming Xu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2014
• Subjects: Algorithm design and analysis; Bayes methods; Bayesian Network structure learning; Big data; Complexity theory; Computational modeling; graphic mode; Graphics; Inference algorithms; partial order; Score function
• DOI: 10.1109/CBD.2014.14

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.