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2017 | OriginalPaper | Chapter

Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics

Authors : Godai Azuma, Daisuke Kitakoshi, Masato Suzuki

Published in: Information Science and Applications 2017

Publisher: Springer Singapore

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Abstract

This paper evaluates a structure learning method for Bayesian networks called Stepwise Structure Learning with Probabilistic pruning (SSL-Pro). Probabilistic pruning allows this method to obtain appropriate network structures while reducing computational time for structure learning. Computer experiments were conducted to investigate the characteristics of the SSL-Pro. Results showed that the SSL-Pro generally provided favorable performance, and revealed several parameter-setting guidelines to ensure reasonable learning.

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Metadata
Title
Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics
Authors
Godai Azuma
Daisuke Kitakoshi
Masato Suzuki
Copyright Year
2017
Publisher
Springer Singapore
Book
Information Science and Applications 2017

Print ISBN: 978-981-10-4153-2

Electronic ISBN: 978-981-10-4154-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-4154-9_62