Advertisement

Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS

  • B. Sierra
  • E. Lazkano
  • I. Inza
  • M. Merino
  • P. Larrañaga
  • J. Quiroga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

The Transjugular Intrahepatic Portosystemic Shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff’s experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first six months after TIPS placement. An investigation for predicting the conduct of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. We have applied a new Estimation of Distribution Algorithms based approach in order to perform a Prototype and Feature Subset Selection to improve the classification accuracy obtained using all the variables and all the cases. Used paradigms are K-Nearest Neighbours, Artificial Neural Networks and Classification Trees.

Keywords

Machine Learning Prototype Selection Feature Subset Selection Transjugular Intrahepatic Portosystemic Shunt Estimation of Distribution Algorithm Indications 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. Aha, D. Kibler y M.K. Albert (1991): Instance-Based learning algorithms. Machine Learning 6, 37–66.Google Scholar
  2. 2.
    P.C. Bornman, J.E.J. Krige and J. Terblanche, Management of oesophageal varices, Lancet 343 (1994) 1079–1084.CrossRefGoogle Scholar
  3. 3.
    M. Cameron-Jones (1995): Instance selection by encoding length heuristic with random mutation hill climbing. IEEE Proceedings of the eighth Australian Joint Conference on Artificial Intelligence, World Scientific, 99–106.Google Scholar
  4. 4.
    B.V. Dasarathy (1991): Nearest Neighbor (NN) Norms: NN Pattern Recognition Classification Techniques. IEEE Computer Society PressGoogle Scholar
  5. 5.
    T.G. Diettrich, Approximate statistical tests for comparing supervised learning algorithms, Neural Computation 10 (1998) 1895–1924.CrossRefGoogle Scholar
  6. 6.
    P. Djouadi, E. Boucktache (1997): A fast algorithm for the Nearest-Neighbor Classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19,No. 3, 277–281.CrossRefGoogle Scholar
  7. 7.
    J. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, 1975).Google Scholar
  8. 8.
    I. Inza, P. Larrañaga, B. Sierra, R. Etxeberria, J.A. Lozano and J.M. Peña, Representing the behaviour of supervised classification learning algorithms by Bayesian networks, Pattern Recognition Letters, 20(11–13) (1999) 1201–1210.CrossRefGoogle Scholar
  9. 9.
    I. Inza, P. Larrañaga, R. Etxeberria and B. Sierra (2000): “Feature subset selection by Bayesian network-based optimization”, Artificial Intelligence 123, 157–184.zbMATHCrossRefGoogle Scholar
  10. 10.
    R. Kohavi and G. John, Wrappers for feature subset selection, Artificial Intelligence 97 (1997) 273–324.zbMATHCrossRefGoogle Scholar
  11. 11.
    P. Larrañaga, R. Etxeberria, J.A. Lozano, B. Sierra, I. Inza, J.M. Peña, A review of the cooperation between evolutionary computation and probabilistic graphical models, in: Proceedings of the II Symposium on Artificial Intelligence CIMAF99, La Habana, Cuba, 1999, pp. 314–324.Google Scholar
  12. 12.
    M. Malinchoc, P.S. Kamath, F.D. Gordon, C.J. Peine, J. Rank and P.C.J. ter Borg, A model to Predict Poor Survival in Patients Undergoing Transjugular Intrahepatic Portosystemic Shunts, Hepatology 31 (2000) 864–871.CrossRefGoogle Scholar
  13. 13.
    T. Mitchell (1997): Machine Learning. McGraw-Hill.Google Scholar
  14. 14.
    H. Müehlenbein and G. Paaü, From recombination of genes to the estimation of distributions. Binary parameters, in: Lecture Notes in Computer Science 1411: Parallel Problem Solving from Nature — PPSN IV (1996) 178–187.Google Scholar
  15. 15.
    M. Pelikan, D.E. Goldberg, F. Lobo, A Survey of Optimization by Building and Using Probabilistic Model, IlliGAL Report 99018, Urbana: University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, 1999.Google Scholar
  16. 16.
    J.R. Quinlan (1993): “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, Inc. Los Altos, CaliforniaGoogle Scholar
  17. 17.
    M. Róssle, V. Siegerstetter, M. Huber and A. Ochs, The first decade of the transjugular intrahepatic portosystemic shunt (TIPS): state of the art, Liver 18 (1998) 73–89.Google Scholar
  18. 18.
    B. Sierra, N. Serrano, P. Larrañaga, E.J. Plasencia, I. Inza, J.J. Jiménez, J.M. Dela Rosa and M.L. Mora (2001): Using Bayesian networks in the construction of a multi-classifier. A case study using Intensive Care Unit patient data. Artificial Intelligence in Medicine. In press.Google Scholar
  19. 19.
    D.B. Skalak (1994): Prototipe and feature selection by Sampling and Random Mutation Hill Climbing Algortithms. Proceedings of the Eleventh International Conference on Machine Learning, NJ. Morgan Kaufmann. 293–301.Google Scholar
  20. 20.
    M. Stone (1974): Cross-validation choice and assessment of statistical procedures. Journal Royal of Statistical Society 36, 111–147.zbMATHGoogle Scholar
  21. 21.
    D.L. Wilson (1972): Asymptotic properties of nearest neighbour rules using edited data. IEEE Transactions on Systems, Man and Cybernetics, Vol. 2, 408–421.zbMATHCrossRefGoogle Scholar
  22. 22.
    J. Zhang (1992): Selecting Typical instances in Instance-Based Learning. Proceedings of the Ninth International Machine Learning Workshop, Aberdeen, Escocia. Morgan-Kaufmann, San Mateo, Ca, 470–479.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • B. Sierra
    • 1
  • E. Lazkano
    • 1
  • I. Inza
    • 1
  • M. Merino
    • 2
  • P. Larrañaga
    • 1
  • J. Quiroga
    • 3
  1. 1.Dept. of Computer Science and Artificial IntelligenceUniversity of the Basque CountryDonostiaSpain
  2. 2.Basque Health Service - Osakidetza, Comarca Gipuzkoa - Este, Avenida NavarraDonostia - San SebastiánSpain
  3. 3.Facultad de MedicinaUniversity Clinic of NavarraPamplona - IruñaSpain

Personalised recommendations