An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning

TL;DR

This paper introduces H2PC, a hybrid algorithm for Bayesian network structure learning that outperforms the state-of-the-art MMHC algorithm in accuracy and structure quality through extensive experiments.

Contribution

The paper presents H2PC, a novel hybrid approach combining constraint-based and score-based methods, demonstrating superior performance over existing algorithms.

Findings

H2PC outperforms MMHC in goodness of fit.

H2PC produces network structures closer to true data dependencies.

Extensive experiments validate H2PC's effectiveness.

Abstract

We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from incremental and divide-and-conquer constraint-based methods to learn the parents and children of a target variable. We conduct an experimental comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning, on several benchmarks with various data sizes. Our extensive experiments show that H2PC outperforms MMHC both in terms of goodness of fit to new data and in terms of the quality of the network structure itself, which is closer to the true dependence structure of the data. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.