Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks

TL;DR

This paper introduces a simple, efficient ordering-based search algorithm for learning Bayesian network structures from data, outperforming standard heuristics and being easier to implement.

Contribution

The paper proposes a novel search method over orderings instead of network structures, reducing complexity and improving performance in Bayesian network learning.

Findings

Outperforms standard greedy hill-climbing with tabu lists

Competitive with more complex recent algorithms

Efficient and easy-to-implement approach

Abstract

One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NP-hard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-to-implement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that ordering-based search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.