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

Godai Azuma; Daisuke Kitakoshi; Masato Suzuki

doi:10.1007/978-981-10-4154-9_62

International Conference on Information Science and Applications

ICISA 2017: Information Science and Applications 2017 pp 533-543

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

Authors
Authors and affiliations

Godai AzumaEmail author
Daisuke Kitakoshi
Masato Suzuki

Conference paper

First Online:: 18 March 2017

DOI: 10.1007/978-981-10-4154-9_62

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 424)

Cite this paper as:: Azuma G., Kitakoshi D., Suzuki M. (2017) Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics. In: Kim K., Joukov N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore

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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Authors and Affiliations

Godai Azuma
- 1
Email author
Daisuke Kitakoshi
- 2
Masato Suzuki
- 2

1.Department of Communication Engineering and Informatics, Faculty of Informatics and EngineeringThe University of Electro-CommunicationsChofuJapan
2.Department of Computer ScienceNational Institute of Technology, Tokyo CollegeHachiojiJapan

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

Abstract

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