Learning the Structure of Bayesian Network from Small Amount of Data

Abstract

Many areas of artificial intelligence must handling with imperfection of information. One of the ways to do this is using representation and reasoning with Bayesian networks. Creation of a Bayesian network consists in two stages. First stage is to design the node structure and directed links between them. Choosing of a structure for network can be done either through empirical developing by human experts or through machine learning algorithm. The second stage is completion of probability tables for each node. Using a machine learning method is useful, especially when we have 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 best learning method for small amount of learning data. Means more experiments we drop conclusion of using existent methods for learning a network structure.