A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Author
Keywords
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
Year of Publication
2014
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
3
Issue
Regular Issue
Number
1
Number of Pages
7-13
Date Published
12/2014
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/journal/sites/default/files/files/2014/11/ijimai20143_1_1_pdf_24199.pdf
DOI
10.9781/ijimai.2014.311
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