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Structure Learning of Bayesian Networks Using a New Unrestricted Dependency Algorithm

Authors:
Sona Taheri
Musa Mammadov

Keywords: Data Mining; Bayesian Networks; Naive Bayes; Tree Augmented Naive Bayes; $k-$Dependency Bayesian Networks; Topological Traversal Algorithm

Abstract:
Bayesian Networks have deserved extensive attentions in data mining due to their efficiencies, and reasonable predictive accuracy. A Bayesian Network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency between two variables. Constructing a Bayesian Network from data is the learning process that is divided in two steps: learning structure and learning parameter. In many domains, the structure is not known a priori and must be inferred from data. This paper presents an iterative unrestricted dependency algorithm for learning structure of Bayesian Networks for binary classification problems. Numerical experiments are conducted on several real world data sets, where continuous features are discretized by applying two different methods. The performance of the proposed algorithm is compared with the Naive Bayes, the Tree Augmented Naive Bayes, and the $k-$Dependency Bayesian Networks. The results obtained demonstrate that the proposed algorithm performs efficiently and reliably in practice.

Pages: 54 to 59

Copyright: Copyright (c) IARIA, 2012

Publication date: October 21, 2012

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-227-1

Location: Venice, Italy

Dates: from October 21, 2012 to October 26, 2012