A Score based approach towards improving Bayesian Network Structure Learning

摘要：

　　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.

关键词： Bayesian Network structure learning Score function partial order graphic model Big Data

作者: Yan Tang Zhuoming Xu

作者单位: College of Computer and Information Hohai University Nanjing,210098,China

会议类型: 国际会议

会议名称: 2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)

会议地点: 安徽黄山

会议语种:英文

页码: 39-44

在线出版日期: 2014-11-20（万方平台首次上网日期，不代表论文的发表时间）