论文标题
通过修剪候选父母组合的高维贝叶斯网络结构的近似学习
Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets
论文作者
论文摘要
学习贝叶斯网络（BN）结构的基于分数的算法提供了从不同级别的近似学习到精确学习的解决方案。之所以存在近似解决方案，是因为精确学习通常不适用于中等或更高复杂性的网络。通常，近似解决方案倾向于为速度牺牲准确性，在这种速度中，目的是最大程度地减少准确性的损失并最大程度地提高速度的增长。虽然优化了一些近似算法以处理数千个变量，但这些算法仍然无法学习如此高维结构。一些最有效的基于得分的算法将结构学习问题作为候选父母组合的组合优化。本文探讨了针对高维问题的候选父母组合规模的策略。结果说明了不同水平的修剪水平如何相对于模型拟合的准确性损失影响学习速度，并表明可能需要进行积极的修剪来为高复杂性问题产生近似解决方案。
Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for speed, where the aim is to minimise the loss in accuracy and maximise the gain in speed. While some approximate algorithms are optimised to handle thousands of variables, these algorithms may still be unable to learn such high dimensional structures. Some of the most efficient score-based algorithms cast the structure learning problem as a combinatorial optimisation of candidate parent sets. This paper explores a strategy towards pruning the size of candidate parent sets, aimed at high dimensionality problems. The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
