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Disturbing Neighbors Ensembles for Linear SVM

  • Jesús Maudes
  • Juan J. Rodríguez
  • César García-Osorio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

Ensembles need their base classifiers do not always agree for any prediction (diverse base classifiers). Disturbing Neighbors (\(\mathcal{DN}\)) is a method for improving the diversity of the base classifiers of any ensemble algorithm. \(\mathcal{DN}\) builds for each base classifier a set of extra features based on a 1-Nearest Neighbors (1-NN) output. These 1-NN are built using a small subset of randomly selected instances from the training dataset. \(\mathcal{DN}\) has already been proved successfully on unstable base classifiers (i.e. decision trees). This paper presents an experimental validation on 62 UCI datasets for standard ensemble methods using Support Vector Machines (SVM) with a linear kernel as base classifiers. SVMs are very stable, so it is hard to increase their diversity when they belong to an ensemble. However, experiments will show that \(\mathcal{DN}\) usually improves ensemble accuracy and base classifiers diversity.

Keywords

SVM Ensembles Diversity Disturbing Neighbors Kappa-Error Movement Diagrams 

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References

  1. 1.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)zbMATHGoogle Scholar
  2. 2.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  3. 3.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann, San Francisco (1996)Google Scholar
  4. 4.
    Vapnik, V.N.: The Nature of Statistical Learning Theory (Information Science and Statistics). Springer, Heidelberg (1999)Google Scholar
  5. 5.
    Lin, C.: Liblinear (2008), http://mloss.org/software/view/61/
  6. 6.
    Maudes, J., Rodríguez, J.J., García-Osorio, C.: Disturbing neighbors diversity for decision forests. In: Okun, O., Valentini, G. (eds.) Workshop on Supervised and Unsupervised Ensemble Methods and their Applications, SUEMA 2008, pp. 67–71 (2008)Google Scholar
  7. 7.
    Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005), http://www.cs.waikato.ac.nz/ml/weka zbMATHGoogle Scholar
  8. 8.
    Webb, G.I.: Multiboosting: A technique for combining boosting and wagging. Machine Learning 40(2) (2000)Google Scholar
  9. 9.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: support vector learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  10. 10.
    Domeniconi, C., Yan, B.: Nearest neighbor ensemble. In: ICPR, vol. (1), pp. 228–231 (2004)Google Scholar
  11. 11.
    Caprile, B., Merler, S., Furlanello, C., Jurman, G.: Exact bagging with k-nearest neighbour classifiers. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 72–81. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36(1-2), 105–139 (1999)CrossRefGoogle Scholar
  13. 13.
    Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~MLearn/MLRepository.html
  14. 14.
    Dietterich, T.G.: Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation 10(7), 1895–1923 (1998)CrossRefGoogle Scholar
  15. 15.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. In: Proc. 14th International Conference on Machine Learning, pp. 211–218. Morgan Kaufmann, San Francisco (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jesús Maudes
    • 1
  • Juan J. Rodríguez
    • 1
  • César García-Osorio
    • 1
  1. 1.University of BurgosSpain

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