The FEDHC Bayesian network learning algorithm

TL;DR

The paper introduces FEDHC, a new hybrid Bayesian network learning algorithm that is efficient, adaptable to different data types, and robust to outliers, with demonstrated superior performance in simulations and real data applications.

Contribution

A novel hybrid Bayesian network learning algorithm, FEDHC, with an efficient R implementation and robustness for continuous data, improving upon existing methods like MMHC.

Findings

FEDHC is computationally efficient in simulations.

FEDHC achieves higher or comparable accuracy to MMHC and PCHC.

Application to economic data demonstrates practical utility.

Abstract

The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.