Transfer learning for nonparametric Bayesian networks

2026-04-01

AI Summary

This study addresses the challenges of structure learning for nonparametric Bayesian networks under data scarcity, where conventional approaches suffer from limited performance and standard transfer learning often induces negative transfer. To overcome these limitations, this work systematically introduces transfer learning into nonparametric Bayesian network learning and proposes two novel methods: a constraint-based PCS-TL and a score-based HC-TL, which integrate respectively into the PC-stable and hill-climbing frameworks. Both methods incorporate kernel density estimation and log-linear pooling for parameter fusion. Additionally, dedicated metrics are designed to detect and mitigate negative transfer. Experimental results on synthetic and UCI datasets demonstrate that the proposed approaches significantly outperform baseline methods, effectively enhancing learning accuracy and robustness in small-sample settings while accelerating industrial deployment.

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.

Problem

• transfer learning
• nonparametric Bayesian networks
• scarce data
• negative transfer

Innovation

• transfer learning
• nonparametric Bayesian networks
• negative transfer
• structure learning
• log-linear pooling