New article in Research in Computer Science

A. Platas-López, N. Cruz-Ramírez, E. Mezura-Montes, and A. Guerra-Hernández. Discriminative Parameter Learning of Bayesian Networks using differential evolution: A preliminary analysis. Research in Computing Science, 149(3):75–82, 2020.

Abstract. This work proposes Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Although Discriminative Parameter Learning algorithms have been proposed, to the best of the authors’ knowledge, a metaheuristic approach has not been devised yet. Thus, the objective of this research is to come up with this kind of solution and evaluate its behavior so that its feasibility in this domain can be determined. According to the theory such a solution tends to generate low-bias classifiers that minimize classification error but this is not reflected in results, regarding proposed method, bias in search for best solutions improves DEs performance.