Abstract
This paper introduces two transfer learning methodologies for estimating nonparametric Bayesian networks under scarce data. We propose two algorithms, a constraint-based structure learning method, called PC-stable-transfer learning (PCS-TL), and a score-based method, called hill climbing transfer learning (HC-TL). We also define particular metrics to tackle the negative transfer problem in each of them, a situation in which transfer learning has a negative impact on the model's performance. Then, for the parameters, we propose a log-linear pooling approach. For the evaluation, we learn kernel density estimation Bayesian networks, a type of nonparametric Bayesian network, and compare their transfer learning performance with the models alone. To do so, we sample data from small, medium and large-sized synthetic networks and datasets from the UCI Machine Learning repository. Then, we add noise and modifications to these datasets to test their ability to avoid negative transfer. To conclude, we perform a Friedman test with a Bergmann-Hommel post-hoc analysis to show statistical proof of the enhanced experimental behavior of our methods. Thus, PCS-TL and HC-TL demonstrate to be reliable algorithms for improving the learning performance of a nonparametric Bayesian network with scarce data, which in real industrial environments implies a reduction in the required time to deploy the network.
Abstract (translated)
本文介绍了两种在数据稀缺条件下用于估计非参数贝叶斯网络的迁移学习方法。我们提出了两种算法：一种是基于约束的结构学习方法，称为PC稳定迁移学习（PCS-TL）；另一种是基于评分的方法，称为爬山迁移学习（HC-TL）。同时，我们为每种方法定义了特定指标以解决负迁移问题——即迁移学习对模型性能产生负面影响的情况。对于参数估计，我们提出了一种对数线性池化方法。在评估阶段，我们学习了核密度估计贝叶斯网络（一种非参数贝叶斯网络），并将其迁移学习性能与单独模型进行比较。为此，我们从小型、中型和大型合成网络以及UCI机器学习库的数据集中采样数据，并通过对这些数据集添加噪声和修改来测试其避免负迁移的能力。最后，我们进行了弗里德曼检验并结合伯格曼-霍梅尔事后分析，以统计学证明我们方法的实验性能提升。因此，PCS-TL和HC-TL证明是可靠的算法，能够改善数据稀缺条件下非参数贝叶斯网络的学习性能，这在真实工业环境中意味着减少网络部署所需的时间。
URL
https://arxiv.org/abs/2604.01021
