OPTIMIZED ALGORITHM FOR LEARNING BAYESIAN NETWORK SUPER-STRUCTURES

Edwin Villanueva, Carlos Dias Maciel

Abstract

Estimating super-structures (SS) as structural constraints for learning Bayesian networks (BN) is an important step of scaling up these models to high-dimensional problems. However, the literature has shown a lack of algorithms with an appropriate accuracy for such purpose. The recent Hybrid Parents and Children - HPC (De Morais and Aussem, 2010) has shown an interesting accuracy, but its local design and high computational cost discourage its use as SS estimator. We present here the OptHPC, an optimized version of HPC that implements several optimizations to get an efficient global method for learning SS. We demonstrate through several experiments that OptHPC estimates SS with the same accuracy than HPC in about 30% of the statistical tests used by it. Also, OptHPC showed the most favorable balance sensitivity/specificity and computational cost for use as super-structure estimator when compared to several state-of-the-art methods.

References

  1. De Morais, S. R. and Aussem, A. (2010). An efficient and scalable algorithm for local bayesian network structure discovery. In European conference on Machine learning and knowledge discovery in databases: Part III, ECML PKDD'10, pages 164-179. SpringerVerlag.
  2. Kojima, K., Perrier, E., Imoto, S., and Miyano, S. (2010). Optimal search on clustered structural constraint for learning bayesian network structure. J. Mach. Learn. Res., 11:285-310.
  3. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Morgan Kaufmann.
  4. Pena, J. M., Nilsson, R., Bjorkegren, J., and Tegner, J. (2007). Towards scalable and data efficient learning of Markov boundaries. Int. J. Approx. Reasoning, 45(2):211-232.
  5. Perrier, E., Imoto, S., and Miyano, S. (2008). Finding Optimal Bayesian Network Given a Super-Structure. J. Mach. Learn. Res., 9:2251-2286.
  6. Pourret, O., Nam, P., Naïm, P., Marcot, B., and Na?m, P. (2008). Bayesian Networks: A Practical Guide to Applications. Statistics in Practice. John Wiley & Sons.
  7. Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, prediction, and search. The MIT Press, Cambridge, second edition.
  8. Tsamardinos, I., Brown, L. E., and Aliferis, C. F. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn., 65(1):31-78.
  9. Wang, M., Chen, Z., and Cloutier, S. (2007). A hybrid Bayesian network learning method for constructing gene networks. Comput. Biol. Chem., 31(5-6):361- 372.
  10. Wong, M. and Leung, K. (2004). An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach. IEEE Trans. Evol. Comput., 8(4):378-404.
Download


Paper Citation


in Harvard Style

Villanueva E. and Maciel C. (2012). OPTIMIZED ALGORITHM FOR LEARNING BAYESIAN NETWORK SUPER-STRUCTURES . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 217-222. DOI: 10.5220/0003785402170222


in Bibtex Style

@conference{icpram12,
author={Edwin Villanueva and Carlos Dias Maciel},
title={OPTIMIZED ALGORITHM FOR LEARNING BAYESIAN NETWORK SUPER-STRUCTURES},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={217-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003785402170222},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - OPTIMIZED ALGORITHM FOR LEARNING BAYESIAN NETWORK SUPER-STRUCTURES
SN - 978-989-8425-98-0
AU - Villanueva E.
AU - Maciel C.
PY - 2012
SP - 217
EP - 222
DO - 10.5220/0003785402170222