DOI
10.9781/ijimai.2014.311
Abstract
Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning), and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks
Source Publication
International Journal of Interactive Multimedia and Artificial Intelligence
Recommended Citation
Fukuda, Sho; Yamanaka, Yuuma; and Yoshihiro, Takuya
(2014)
"A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks,"
International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 3:
Iss.
1, Article 8.
DOI: 10.9781/ijimai.2014.311
Available at:
https://ijimai.researchcommons.org/ijimai/vol3/iss1/8
