Bayesian Network Structure Learning from Big Data: A Reservoir Sampling Based Ensemble Method

Abstract

Bayesian network (BN) learning from big datasets is potentially more valuable than learning from conventional small datasets as big data contain more comprehensive probability distributions and richer causal relationships. However, learning BNs from big datasets requires high computational cost and easily ends in failure, especially when the learning task is performed on a conventional computation platform. This paper addresses the issue of BN structure learning from a big dataset on a conventional computation platform, and proposes a reservoir sampling based ensemble method (RSEM). In RSEM, a greedy algorithm is used to determine an appropriate size of sub datasets to be extracted from the big dataset. A fast reservoir sampling method is then adopted to efficiently extract sub datasets in one pass. Lastly, a weighted adjacent matrix based ensemble method is employed to produce the final BN structure. Experimental results on both synthetic and real-world big datasets show that RSEM can perform BN structure learning in an accurate and efficient way.