Feature Selection for Efficient Local-to-Global Bayesian Network Structure Learning

TL;DR

This paper introduces an efficient feature selection-based method for local-to-global Bayesian network structure learning, significantly reducing computational costs while maintaining competitive accuracy.

Contribution

It proposes the F2SL approach that leverages MRMR feature selection to improve the efficiency of BN structure learning, with two variants using different edge orientation methods.

Findings

F2SL algorithms are more efficient than existing methods.

F2SL achieves comparable structure learning quality.

Experimental results validate the effectiveness of the proposed approach.

Abstract

Local-to-global learning approach plays an essential role in Bayesian network (BN) structure learning. Existing local-to-global learning algorithms first construct the skeleton of a DAG (directed acyclic graph) by learning the MB (Markov blanket) or PC (parents and children) of each variable in a data set, then orient edges in the skeleton. However, existing MB or PC learning methods are often computationally expensive especially with a large-sized BN, resulting in inefficient local-to-global learning algorithms. To tackle the problem, in this paper, we develop an efficient local-to-global learning approach using feature selection. Specifically, we first analyze the rationale of the well-known Minimum-Redundancy and Maximum-Relevance (MRMR) feature selection approach for learning a PC set of a variable. Based on the analysis, we propose an efficient F2SL (feature selection-based structure learning) approach to local-to-global BN structure learning. The F2SL approach first employs the MRMR approach to learn a DAG skeleton, then orients edges in the skeleton. Employing independence tests or score functions for orienting edges, we instantiate the F2SL approach into two new algorithms, F2SL-c (using independence tests) and F2SL-s (using score functions). Compared to the state-of-the-art local-to-global BN learning algorithms, the experiments validated that the proposed algorithms in this paper are more efficient and provide competitive structure learning quality than the compared algorithms.