Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets

TL;DR

This paper proposes a pruning strategy for candidate parent sets in score-based Bayesian network structure learning, enabling faster approximate learning in high-dimensional problems with controlled accuracy loss.

Contribution

It introduces a novel pruning approach to reduce candidate parent sets, improving scalability of Bayesian network learning for high-dimensional data.

Findings

Aggressive pruning accelerates learning significantly.

Pruning levels can be tuned to balance speed and accuracy.

The method enables approximate learning in high-dimensional networks.

Abstract

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for speed, where the aim is to minimise the loss in accuracy and maximise the gain in speed. While some approximate algorithms are optimised to handle thousands of variables, these algorithms may still be unable to learn such high dimensional structures. Some of the most efficient score-based algorithms cast the structure learning problem as a combinatorial optimisation of candidate parent sets. This paper explores a strategy towards pruning the size of candidate parent sets, aimed at high dimensionality problems. The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.