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Learning the Structure of Bayesian Network from Small Amount of Data

Author(s): Adina COCU | Marian Viorel CRACIUN | Bogdan COCU

Journal: Annals of Dunarea de Jos
ISSN 1221-454X

Volume: 32;
Issue: 2;
Start page: 12;
Date: 2009;
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Keywords: Bayesian network | machine learning algorithm | structure learning

Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways to do this is using representation and reasoning withBayesian networks. Creation of a Bayesian network consists in two stages. First stage isto design the node structure and directed links between them. Choosing of a structurefor network can be done either through empirical developing by human experts orthrough machine learning algorithm. The second stage is completion of probabilitytables for each node. Using a machine learning method is useful, especially when wehave a big amount of leaning data. But in many fields the amount of data is small,incomplete and inconsistent. In this paper, we make a case study for choosing the bestlearning method for small amount of learning data. Means more experiments we dropconclusion of using existent methods for learning a network structure.
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