Abstract
This paper proposes a novel method for learning Bayesian networks
from incomplete databases in the presence of missing values, which
combines an evolutionary algorithm with the traditional Expectation
Maximization (EM) algorithm. A data completing procedure is presented
for learning and evaluating the candidate networks. Moreover, a strategy
is introduced to obtain better initial networks to facilitate the
method. The new method can also overcome the problem of getting stuck
in sub-optimal solutions which occurs in most existing learning algorithms.
The experimental results on the databases generated from several
benchmark networks illustrate that the new method has better performance
than some state-of-the-art algorithms. We also apply the method to
a data mining problem and compare the performance of the discovered
Bayesian networks with the models generated by other learning algorithms.
The results demonstrate that our method outperforms other algorithms.
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