Author

J. Puerta

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#machine learning Open access Sep 2026

Federated Learning of AnDE Classifiers

Experiments show that discriminative models with $n \geq 1$ consistently outperform federated Naive Bayes, and that privacy-preserving aggregation is effective with limited accuracy loss, establishing federated AnDE as a viable and privacy-preserving framework.

Pablo Torrijos, J. C. Alfaro, José A. Gámez et al. · PKDD/ECML Workshops · 0 citations

#machine learning Open access Jun 2024

Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms

Experimental evaluation demonstrates the genetic algorithm's ability to obtain consensus BNs with limited treewidth, providing a valuable tool for aggregating information from diverse sources while returning a computationally actionable model.

Pablo Torrijos, José A. Gámez, J. Puerta · IEEE Congress on Evolutionar... · 3 citations