dplbnDE: An R package for discriminative parameter learning of Bayesian Networks by Differential Evolution

Abstract

The dplbnDE R package is a novel tool that implements Differential Evolution strategies for training Bayesian Network parameters using Discriminative Learning. Focusing on optimizing the Conditional Log-Likelihood rather than the log-likelihood, dplbnDE enhances the performance of Bayesian Networks models in various applications. The package offers four main functions (DErand, DEbest, jade, and lshade) that implement different DE variants, providing users with a versatile and efficient approach to Bayesian Network parameter learning. dplbnDE has the potential to impact data-driven industries by improving predictive capabilities and decision-making processes in fields such as healthcare, finance, and supply chain management. The package and its code are made freely available.

Keywords

• Bayesian Networks
• Discriminative Learning
• Differential Evolution
• Parameter optimization