Abstract
Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of good Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In this paper, we propose a new algorithm to learn Bayesian network structure based on a genetic algorithm that evolves probability vectors. Through performance evaluation, we found that this probability-based approach is effective to learn better Bayesian network structure with less time.
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Fukuda, S., Yoshihiro, T. (2014). Learning Bayesian Networks Using Probability Vectors. In: Omatu, S., Bersini, H., Corchado, J., RodrÃguez, S., Pawlewski, P., Bucciarelli, E. (eds) Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-07593-8_58
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DOI: https://doi.org/10.1007/978-3-319-07593-8_58
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07592-1
Online ISBN: 978-3-319-07593-8
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