Approach of variable clustering and compression for learning large Bayesian networks

TL;DR

This paper introduces a novel method for learning large Bayesian network structures by clustering features and using compressed information to enable faster, potentially parallelized structure learning with maintained accuracy.

Contribution

It presents a new approach combining feature space clustering with information compression to improve the efficiency of large Bayesian network structure learning.

Findings

Enhanced speed of structure learning.

Maintained accuracy with compressed data.

Applicable to parallel processing environments.

Abstract

This paper describes a new approach for learning structures of large Bayesian networks based on blocks resulting from feature space clustering. This clustering is obtained using normalized mutual information. And the subsequent aggregation of blocks is done using classical learning methods except that they are input with compressed information about combinations of feature values for each block. Validation of this approach is done for Hill-Climbing as a graph enumeration algorithm for two score functions: BIC and MI. In this way, potentially parallelizable block learning can be implemented even for those score functions that are considered unsuitable for parallelizable learning. The advantage of the approach is evaluated in terms of speed of work as well as the accuracy of the found structures.