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Towards a More E.cient Evolutionary Induction of Bayesian Networks

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Parallel Problem Solving from Nature — PPSN VII (PPSN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2439))

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Abstract

Bayesian networks (BNs) constitute a useful tool to model the joint distribution of a set of random variables of interest. This paper is concerned with the network induction problem. We propose a number of hybrid recombination operators for extracting BNs from data. These hybrid operators make use of phenotypic information in order to guide the processing of information during recombination. The performance of these new operators is analyzed with respect to that of their genotypic counterparts. It is shown that these hybrid operators provide notably improved and rather robust results. Some remarks on the future of the area are also laid out.

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References

  1. S.A. Andersson, D. Madigan, and M.D. Perlman. A characterization of markov equivalence classes for acyclic digraphs. Annals of Statistics, 25:505–541, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  2. Th. Bäck, D.B. Fogel, and Z. Michalewicz. Handbook of Evolutionary Computation. Oxford University Press, New York NY, 1997.

    MATH  Google Scholar 

  3. I.A. Beinlich, H.J. Suermondt, R.M. Chavez, and G.F. Cooper. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In J. Hunter, J. Cookson, and J. Wyatt, editors, Proceedings of the Second European Conference on Artificial Intelligence and Medicine, pages 247–256, Berlin, 1989. Springer-Verlag.

    Google Scholar 

  4. J. Binder, D. Koller, S. Russell, and K. Kanazawa. Adaptive probabilistic networks with hidden variables. Machine Learning, 29:213–244, 1997.

    Article  MATH  Google Scholar 

  5. D.M. Chickering, D. Geiger, and D. Heckermann. Learning bayesian networks is NP-complete. In D. Fisher and H.-J. Lenz, editors, Learning from data: AI and Statistics V, pages 121–130, New York NY, 1996. Springer-Verlag.

    Google Scholar 

  6. D.M. Chickering. Learning equivalence classes of bayesian-network structures. Submitted manuscript, 2001.

    Google Scholar 

  7. G. Cooper and E. Herskovits. A bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347, 1992.

    MATH  Google Scholar 

  8. C. Cotta and J.M. Troya. Analyzing directed acyclic graph recombination. In B. Reusch, editor, Computational Intelligence: Theory and Applications, volume 2206 of Lecture Notes in Computer Science, pages 739–748. Springer-Verlag, Berlin Heidelberg, 2001.

    Chapter  Google Scholar 

  9. N. Friedman, I. Nachman, and D. Peér. Learning bayesian network structures from massive datasets: The sparse candidate algorithm. In H. Dubios and K. Laskey, editors, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 206–215, San Francisco CA, 1999. Morgan Kaufmann.

    Google Scholar 

  10. D. Geiger, D. Heckerman, and C. Meek. Asymptotic model selection for directed networks with hidden variables. In E. Horvitz and F.V. Jensen, editors, Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, pages 283–290, San Francisco CA, 1996. Morgan Kaufmann.

    Google Scholar 

  11. D. Heckerman, D. Geiger, and D.M. Chickering. Learning bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20(3):197–243, 1995.

    MATH  Google Scholar 

  12. D. Heckerman. A tutorial on learning with bayesian networks. In M.I. Jordan, editor, Learning in Graphical Models, pages 301–354. Kluwer, Dordrecht, 1998.

    Google Scholar 

  13. P. Larrañaga, C.M.H. Kuijpers, R.H. Murga, and Y. Yurramendi. Learning bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 26(4):487–493, 1996.

    Article  Google Scholar 

  14. P. Larrañaga, M. Poza, Y. Yurramendi, R.H. Murga, and C.M. H. Kuijpers. Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(9):912–926, 1996.

    Article  Google Scholar 

  15. N.J. Radcliffe. Equivalence class analysis of genetic algorithms. Complex Systems, 5:183–205, 1991.

    MATH  MathSciNet  Google Scholar 

  16. M.L. Wong, W. Lam, and K.S. Leung. Using evolutionary programming and minimum description length principle for data mining of bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(2):174–178, 1999.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. Lenguajes y Ciencias de la Computación, ETSI Informática University of Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  2. Grupo de Estadística y Ciencias de la Decisión, ESCET University Rey Juan Carlos, 28933, Móstoles, Spain

    Jorge Muruzábal

Authors
  1. Carlos Cotta
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  2. Jorge Muruzábal
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Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática, 18071, Granada, Spain

    Juan Julián Merelo Guervós

  2. Department of Informatics, Technological Educational Institute of Thessaloniki, 54101, Thessaloniki, Greece

    Panagiotis Adamidis

  3. Department of Informatics XI, University of Dortmund, 44221, Dortmund, Germany

    Hans-Georg Beyer  & Hans-Paul Schwefel  & 

  4. Department of Signal Theory and Communications, Universidad Carlos III, Madrid, Spain

    José-Luis Fernández-Villacañas

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© 2002 Springer-Verlag Berlin Heidelberg

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Cotta, C., Muruzábal, J. (2002). Towards a More E.cient Evolutionary Induction of Bayesian Networks. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_70

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  • DOI: https://doi.org/10.1007/3-540-45712-7_70

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44139-7

  • Online ISBN: 978-3-540-45712-1

  • eBook Packages: Springer Book Archive

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