Transfer learning for nonparametric Bayesian networks
This work addresses the challenge of deploying nonparametric Bayesian networks in real industrial environments where data is scarce, offering incremental improvements over existing methods.
The paper tackles the problem of estimating nonparametric Bayesian networks with scarce data by introducing two transfer learning algorithms, PCS-TL and HC-TL, which include metrics to avoid negative transfer and a log-linear pooling approach for parameters, and demonstrates through experiments on synthetic and UCI datasets that these methods reliably improve learning performance, reducing deployment time in industrial settings.
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.