Bayesian network learning on algorithm based on ant colony optimization

Xiao Guang Gao, Huan Huan Zhao, Jia Ren

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Accordering to the hybrid Bayesian networks learning algorithms which are easy to narrow the search space and fall into local optimum, a Bayesian network learning algorithm based on ant colony optimization is proposed. Firstly, this paper applies max-min parents and children (MMPC) to construct the framework of the undirected network, and then uses ant colony optimization to score-search, by balancing the "exploitation" and "exploration" to repair the search space and determine the direction of edges in the network. Finally applying the algorithm to learn a logical alarm reduction mechanism (ALARM) network shows that it reduces the number of missing edges, and gets closer to the real structure of Bayesian network.

Original languageEnglish
Pages (from-to)1509-1512
Number of pages4
JournalXi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
Volume32
Issue number7
DOIs
StatePublished - Jul 2010

Keywords

  • Ant colony optimization
  • Bayesian networks
  • Structure learning

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