A method based on genetic algorithms and fuzzy logic to induce bayesian networks
María Morales, R. Garza Dominguez, Natalie Ramirez, A. Guerra Hernandez, Jose Luis Jiménez-Andrade · 2004
A method to induce Bayesian networks from data to overcome 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.