摘要：Learning a Bayesian network from a numeric set of data is a
challenging task because of dual nature of learning process: initial need to
learn network structure, and then to find out the distribution probability
tables. In this paper, we propose a machine-learning algorithm based on hill
climbing search combined with Tabu list. The aim of learning process is to
discover the best network that represents dependences between nodes. Another
issue in machine learning procedure is handling numeric attributes. In order to
do that, we must perform an attribute discretization pre-processes. This
discretization operation can influence the results of learning network
structure. Therefore, we make a comparative study to find out the most suitable
combination between discretization method and learning algorithm, for a specific
data set.
