dc.description.abstract | 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. | en_US |