TY - JOUR
KW - Bayesian Network
KW - Evolutionary Algorithm
KW - PBIL
AU - Sho Fukuda
AU - Yuuma Yamanaka
AU - Takuya Yoshihiro
AB - 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
IS - Regular Issue
M1 - 1
N2 - 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
PY - 2014
SP - 7
EP - 13
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
UR - http://www.ijimai.org/journal/sites/default/files/files/2014/11/ijimai20143_1_1_pdf_24199.pdf
VL - 3
SN - 1989-1660
ER -