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

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

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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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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