Strategies for improving the modeling and interpretability of Bayesian networks
Data & Knowledge Engineering
Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
IEICE - Transactions on Information and Systems
Algorithm for graphical Bayesian modeling based on multiple regressions
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
A Multi-Objective Evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling
Computers & Mathematics with Applications
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A method to induce bayesian networks from data to over-come some limitations of other learning algorithms is proposed. One of the main features of this method is a metric to evaluate bayesian networks combining different quality criteria. A fuzzy system is proposed to enable the combination of different quality metrics. In this fuzzy system a metric of classification is also proposed, a criterium that is not often used to guide the search while learning bayesian networks. Finally, the fuzzy system is integrated to a genetic algorithm, used as a search method to explore the space of possible bayesian networks, resulting in a robust and flexible learning method with performance in the range of the best learning algorithms of bayesian networks developed up to now.