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

Conference paper

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.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

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

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