One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration often depends on multiple factors, including the size and density of the usually unknown underlying true graph, the sample size of the input data, and the structure learning algorithm. We propose a novel hyperparameter tuning method, called the Out-of-sample Tuning for Structure Learning (OTSL), that employs out-of-sample and resampling strategies to estimate the optimal hyperparameter configuration for structure learning, given the input data set and structure learning algorithm. Synthetic experiments show that employing OTSL as a means to tune the hyperparameters of hybrid and score-based structure learning algorithms leads to improvements in graphical accuracy compared to the state-of-the-art. We also illustrate the applicability of this approach to real datasets from different disciplines.

翻译：实践者在将结构学习算法应用于自身数据时所面临的挑战之一，在于需要确定一组超参数；否则将默认采用一组超参数默认值。最优超参数配置通常取决于多种因素，包括通常未知的底层真实图结构的大小与密度、输入数据的样本量以及结构学习算法本身。我们提出了一种新型超参数调优方法——面向结构学习的样本外调参（OTSL），该方法利用样本外与重采样策略，根据输入数据集和结构学习算法来估计最优超参数配置。合成实验表明，采用OTSL对混合型及基于评分的结构学习算法进行超参数调优，与当前最先进方法相比能够提升图结构的准确性。我们还通过来自不同学科的真实数据集验证了该方法的适用性。