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Towards Big Data Bayesian Network Learning - An Ensemble Learning Based Approach

机译：迈向大数据贝叶斯网络学习-基于集成学习的方法

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摘要

Recently, we are entering the Big Data era[[1]]. The Bayesian Network (BN), as a directed probabilistic graph model, is providing intuitive knowledge presentation and accurate prediction for many mission critical areas. However, the current algorithms do not scale well for Big Data Bayesian network learning. This paper proposes a novel parallel BN learning algorithm called PENBays (Parallel ENsemble based Bayesian Networks Learning), which integrates the best BN learning algorithms MMHC, TPDA and REC. It has three phases: Data Preprocess (DP), Individual Ensemble Learning (IEL) and Central Ensemble Learning (CNL). Through these phases, PENBays effectively learns a BN rapidly from large datasets. Experiments reveal that PENBays learns BNs with better accuracy than base line learning algorithms like MMHC, TPDA and REC, showing promising application potential in the big data mining area.
机译：最近，我们正在进入大数据时代[1]。贝叶斯网络（BN）作为有向概率图模型，正在为许多任务关键区域提供直观的知识表示和准确的预测。但是，当前算法对于大数据贝叶斯网络学习不能很好地扩展。本文提出了一种新颖的并行BN学习算法，称为PENBays（基于并行ENsemble的贝叶斯网络学习），该算法集成了最佳的BN学习算法MMHC，TPDA和REC。它分为三个阶段：数据预处理（DP），个人整体学习（IEL）和中央整体学习（CNL）。在这些阶段中，PENBays有效地从大型数据集中快速学习了BN。实验表明，与诸如MMHC，TPDA和REC之类的基线学习算法相比，PENBays学习BN的准确性更高，显示了在大数据挖掘领域的广阔应用前景。

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