Better Initialization Heuristics for Order-based Bayesian Network Structure Learning

Abstract

An effective approach for learning Bayesian network structures is to
perform a greedy search on the space of variable orderings using a
restricted space of parent sets. Typically, the search is initialized
with a randomly generated ordering. This can lead to poor local optima
and hurt the performance of the method. In this article we develop
informed heuristics for generating initial solutions to order-based
structure learning search. Experiments with a large collection of
real-world data sets demonstrate that our heuristics increase the
quality of the solutions found with a negligible overhead.