The FEDHC Bayesian network learning algorithm
This work provides a more efficient and robust Bayesian network learning algorithm for researchers and practitioners working with diverse data types, addressing the computational cost of existing methods.
This paper introduces FEDHC, a new hybrid Bayesian network learning algorithm for both continuous and categorical variables. Through Monte Carlo simulations, FEDHC is shown to be computationally efficient and achieve similar or higher accuracy compared to MMHC and PCHC.
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}.