Advertisement

Bagging Decision Multi-trees

  • Vicent Estruch
  • César Ferri
  • José Hernández-Orallo
  • Maria José Ramírez-Quintana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3077)

Abstract

Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. A well-known method for generating hypothesis ensembles is Bagging. One of the main drawbacks of ensemble methods in general, and Bagging in particular, is the huge amount of computational resources required to learn, store, and apply the set of models. Another problem is that even using the bootstrap technique, many simple models are similar, so limiting the ensemble diversity. In this work, we investigate an optimization technique based on sharing the common parts of the models from an ensemble formed by decision trees in order to minimize both problems. Concretely, we employ a structure called decision multi-tree which can contain simultaneously a set of decision trees and hence consider just once the ”repeated” parts. A thorough experimental evaluation is included to show that the proposed optimisation technique pays off in practice.

Keywords

Ensemble Methods Decision Trees Bagging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  2. 2.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  3. 3.
    Dietterich, T.G.: Ensemble methods in machine learning. In: First International Workshop on Multiple Classifier Systems, pp. 1–15 (2000)Google Scholar
  4. 4.
    Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, Boosting, and Randomization. Machine Learning 40(2), 139–157 (2000)CrossRefGoogle Scholar
  5. 5.
    Estruch, V., Ferri, C., Hernández, J., Ramírez, M.J.: SMILES: A multi-purpose learning system. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 529–532. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Estruch, V., Ferri, C., Hernández, J., Ramírez, M.J.: Shared Ensembles using Multi-trees. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 204–213. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez, M.J.: Beam Search Extraction and Forgetting Strategies on Shared Ensembles. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 13th International Conference on Machine Learning, pp. 146–148. Morgan Kaufmann, San Francisco (1996)Google Scholar
  9. 9.
    Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-12(10), 993–1001 (1990)CrossRefGoogle Scholar
  10. 10.
    Kohavi, R., Kunz, C.: Option decision trees with majority votes. In: Proc. 14th International Conference on Machine Learning, pp. 161–169. Morgan Kaufmann, San Francisco (1997)Google Scholar
  11. 11.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  12. 12.
    Nilsson, N.J.: Artificial Intelligence: a new synthesis. Morgan Kaufmann, San Francisco (1998)zbMATHGoogle Scholar
  13. 13.
    Parmanto, B., Munro, P.W., Doyle, H.R.: Improving committee diagnosis with resampling techniques. In: Advances in Neural Information Processing Systems, vol. 8, pp. 882–888. The MIT Press, Cambridge (1996)Google Scholar
  14. 14.
    Pearl, J.: Heuristics: Intelligence search strategies for computer problem solving. Addison Wesley, Reading (1985)Google Scholar
  15. 15.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  16. 16.
    Quinlan, J.R.: Bagging, Boosting, and C4.5. In: Proc. of the 30th Nat. Conf. on A.I. and the 8th Innovative Applications of A.I. Conf., pp. 725–730. AAAI Press/MIT Press (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Vicent Estruch
    • 1
  • César Ferri
    • 1
  • José Hernández-Orallo
    • 1
  • Maria José Ramírez-Quintana
    • 1
  1. 1.DSICUniv. Politècnica de ValènciaValènciaSpain

Personalised recommendations