Learning Bayesian Networks by Genetic Algorithms: A case study in the prediction of survival in malignant skin melanoma

  • Pedro Larrañaga
  • Basilio Sierra
  • Miren J. Gallego
  • Maria J. Michelena
  • Juan M. Picaza
Probabilistic Models and Fuzzy Logic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)


In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem. The methodology is applied to the problem of predicting survival of people after one, three and five years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained model, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In both cases, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.


Genetic Algorithm Bayesian Network Basque Country Bayesian Network Structure Learn Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andersen, S.K., Olesen, K.G., Jensen, F.V. and Jensen, F.: ”HUGIN — a shell for building Bayesian belief universes for expert systems”. In Eleventh International Joint Conference on Artificial Intelligence (1989) 1128–1133Google Scholar
  2. 2.
    Beinlinch, I.A., Suermondt, H.J., R.M. Chavez R. M. and Cooper G.F.: “The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks”. In Proceedings of the Second European Conference on Artificial Intelligence in Medicine (1989) 247–256Google Scholar
  3. 3.
    Bouckaert, R.R.: “Optimizing causal orderings for generating DAGs from data”. In Uncertainty in Artificial Intelligence. Proceedings of the Eighth Conference (1992) 9–16Google Scholar
  4. 4.
    Bouckaert, R.R.: “Properties of Bayesian belief networks learning algorithms”. In Uncertainty in Artificial Intelligence. Tenth Annual Conference, (1994) 102–109Google Scholar
  5. 5.
    Cooper, G.F., and Herskovits, E.A.: “A Bayesian method for the induction of probabilistic networks from data”. Machine Learning 9 (1992) 309–347Google Scholar
  6. 6.
    Goldberg, D.E.: ”Genetic Algorithms in Search, Optimization and Machine Learning”. Addison-Wesley, Reading, MA (1989)Google Scholar
  7. 7.
    Heckerman, D., Geiger, D. and Chickering, D.M.: “Learning Bayesian networks: The combination of knowledge and statistical data”. Technical Report MSR-TR-94-09, Microsoft (1994)Google Scholar
  8. 8.
    Izarzugaza, M.I.: ”Informe del registro de Cáncer de Euskadi 1990”. Osasunkaria (1994) 8–11Google Scholar
  9. 9.
    Jensen, F. V.: ”Introduction to Bayesian networks”. University College of London (1996)Google Scholar
  10. 10.
    Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R., and Kuijpers, C.: ”Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 18 (1996) 912–926Google Scholar
  11. 11.
    Larrañaga, P., Murga, R., Poza, M., and Kuijpers, C.: ”Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms”. In Learning from Data: AI and Statistics V, Lecture Notes in Statistics 112. D. Fisher, H.-J. Lenz (eds.), New York, NY: Spriger-Verlag, (1996) 165–174.Google Scholar
  12. 12.
    Larrañaga, P., Kuijpers, C., Murga, R., and Yurramendi, Y.: ”Learning Bayesian Network Structures by searching for the best ordering with genetic algorithms”. IEEE Transactions on System, Man and Cybernetics 26 (1996) 487–493Google Scholar
  13. 13.
    Lauritzen, S.L.: ”Graphical Models”. Oxford University Press (1996)Google Scholar
  14. 14.
    Lauritzen, S.L., and Spiegelhalter, D.J.: ”Local computations with probabilities on graphical structures and their application on expert systems”. Journal Royal of Statistical Society B 50 (1988) 157–224Google Scholar
  15. 15.
    Pearl, J.: ”Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference”. Morgan Kaufmann, San Mateo (1988)Google Scholar
  16. 16.
    Provan, G.M., and Singh, M.: ”Learning Bayesian Networks Using Feature Selection”. In Learning from Data: AI and Statistics V, Lecture Notes in Statistics 112. D. Fisher, H.-J. Lenz (eds.), New York, NY: Spriger-Verlag (1996) 291–300Google Scholar
  17. 17.
    Robinson, R. W.: “Counting unlabeled acyclic digraphs”. In C. H. C. Little (ed.) Lectures Notes in Mathematics 622: Combinatorial Mathematics V, Springer-Verlag, New York, (1977) 28–43.Google Scholar
  18. 18.
    Stone, M.: ”Cross-validation choice and assessment of statistical procedures”. Journal Royal of Statistical Society 36 (1974) 111–147Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Pedro Larrañaga
    • 1
  • Basilio Sierra
    • 1
  • Miren J. Gallego
    • 1
  • Maria J. Michelena
    • 2
  • Juan M. Picaza
    • 3
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of the Basque CountrySpain
  2. 2.Oncological Institute of GipuzkoaSpain
  3. 3.Department of Computer Languages and SystemsUniversity of the Basque CountrySpain

Personalised recommendations