Applying MDL in PSO for learning Bayesian networks

Shu Ching Kuo, Hung Jen Wang, Hsiao Yi Wei, Chih Chuan Chen, Sheng Tun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Since learning Bayesian networks from data is difficult, a new approach is proposed. The particle swarm optimization (PSO) and minimum description length (MDL) are combined to obtain a suitable Bayesian network. MDL is the fitness function in this learning algorithm to evaluate the goodness of the network. By adopting MDL, the balance between simplicity and accuracy is assured, which enables the optimal solution for complex models to be found in reasonable time. Base on the MDL principle, the PSO is used to enhance the structure learning in Bayesian networks. Moreover, conditional probabilities associated with the Bayesian networks are then statistically derived from these data. In the end, the Stroke data set is used for testing the efficiency and effectiveness of the stable network. Experimental results show that the proposed approach has a good accuracy than the comparative methods.

Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Number of pages6
Publication statusPublished - 2011
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: 2011 Jun 272011 Jun 30

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584


Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Applying MDL in PSO for learning Bayesian networks'. Together they form a unique fingerprint.

Cite this