Auto-Ensemble Structure Learning of Large Gaussian Bayesian Networks

Shengcai Liu, Hui Ouyang, Cheng Chen, Peng Yang, Yew-Soon Ong, Ke Tang

Keywords: Structure learning, Large Bayesian Network, Ensemble Method

TL;DR: We introduce the idea of using automatically constructed ensembles for learning large BNs.

Abstract: Learning the structure of Bayesian networks (BNs) from data is challenging, especially for datasets involving a large number of variables. The recently proposed divide-and-conquer (D&D) strategies present a promising approach for learning large BNs. However, they still face a main issue of unstable learning accuracy across subproblems. In this work, we introduce the idea of employing structure learning ensemble (SLE), which combines multiple BN structure learning algorithms together, to consistently achieve high learning accuracy across various problems. We further propose an automatic approach called Auto-SLE for constructing near-optimal SLEs, addressing the challenge of manually designing effective SLEs. The automatically constructed SLE is then integrated into a D&D framework. Extensive experiments firmly show the superiority of our method over existing methods in learning large BNs, achieving accuracy improvement usually by 30%∼225% on datasets involving 10,000 variables. Furthermore, our method generalizes very well to datasets with many more variables and different network characteristics than those present in the training data for constructing the SLE. These results indicate the significant potential of employing automatic construction of SLEs for BN structure learning.

Primary Area: causal reasoning

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Submission Number: 9261