Transfer learning for nonparametric Bayesian networks

TL;DR

This paper presents two transfer learning algorithms for nonparametric Bayesian networks, improving learning with limited data and addressing negative transfer issues through novel metrics and evaluation.

Contribution

Introduction of PCS-TL and HC-TL algorithms with metrics to prevent negative transfer, enhancing nonparametric Bayesian network learning in scarce data scenarios.

Findings

PCS-TL and HC-TL outperform models without transfer learning.

Statistical tests confirm the reliability of the proposed methods.

Methods reduce deployment time in industrial environments.

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.