Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017 (2017), Article ID 9023970, 17 pages
https://doi.org/10.1155/2017/9023970
Research Article

A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees

Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

Correspondence should be addressed to Joaquín Abellán; se.rgu.iasced@nallebaj

Received 9 June 2017; Revised 10 October 2017; Accepted 2 November 2017; Published 3 December 2017

Academic Editor: Roberto Natella

Copyright © 2017 Joaquín Abellán et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. J. Hand, Construction and Assessment of Classification Rules, John Wiley and Sons, New York, NY, USA, 1997.
  2. D. J. Hand, Discrimination and Classification, John Wiley, 1981.
  3. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 1993.
  4. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, Boston, Mass, USA, 1988. View at MathSciNet
  5. E. B. Hunt, J. Marin, and P. Stone, in Experiments in Induction, Academic Press, 1966.
  6. J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996. View at Google Scholar · View at Scopus
  8. Y. Freund and R. E. Schapire, “Experiments with a new boosting algorithm,” in Proceedings of the the Thirteenth International Conference on Machine Learning (ICML 1996), L. Saitta, Ed., pp. 148–156, Morgan Kaufmann, 1996.
  9. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. T. G. Dietterich, “Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization,” Machine Learning, vol. 40, no. 2, pp. 139–157, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Melville and R. J. Mooney, “Constructing diverse classifier ensembles using artificial training examples,” in Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI’03, pp. 505–510, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2003, http://dl.acm.org/citation.cfm?id=1630659.1630734.
  12. L.-Y. Dai, C.-M. Feng, J.-X. Liu, C.-H. Zheng, J. Yu, and M.-X. Hou, “Robust nonnegative matrix factorization via joint graph Laplacian and discriminative information for identifying differentially expressed genes,” Complexity, Article ID 4216797, 11 pages, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  13. B. Frénay and M. Verleysen, “Classification in the presence of label noise: A survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 5, pp. 845–869, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Walley, “Inferences from multinomial data: learning about a bag of marbles,” ournal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 3–57, 1996. View at Google Scholar · View at MathSciNet
  15. J. Abellán and S. Moral, “Building Classification Trees Using the Total Uncertainty Criterion,” International Journal of Intelligent Systems, vol. 18, no. 12, pp. 1215–1225, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. G. J. Klir, Uncertainty and Information, Foundations of Generalized Information Theory, Wiley-Interscience, New York, NY, USA, 2006. View at Publisher · View at Google Scholar
  17. J. Abellán, G. J. Klir, and S. Moral, “Disaggregated total uncertainty measure for credal sets,” International Journal of General Systems, vol. 35, no. 1, pp. 29–44, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  18. J. Abellán and S. Moral, “Upper entropy of credal sets. Applications to credal classification,” International Journal of Approximate Reasoning, vol. 39, no. 2-3, pp. 235–255, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  19. J. Abellán and A. R. Masegosa, “A filter-wrapper method to select variables for the naive bayes classifier based on credal decision trees,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 17, no. 6, pp. 833–854, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Abellán and J. G. Castellano, “A comparative study on base classifiers in ensemble methods for credit scoring,” Expert Systems with Applications, vol. 73, pp. 1–10, 2017. View at Publisher · View at Google Scholar · View at Scopus
  21. C. J. Mantas and J. Abellán, “Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data,” Expert Systems with Applications, vol. 41, no. 10, pp. 4625–4637, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. C. J. Mantas, J. Abellán, and J. G. Castellano, “Analysis of Credal-C4.5 for classification in noisy domains,” Expert Systems with Applications, vol. 61, pp. 314–326, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Abellán and A. R. Masegosa, “Bagging schemes on the presence of class noise in classification,” Expert Systems with Applications, vol. 39, no. 8, pp. 6827–6837, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Verbaeten and A. Van Assche, “Ensemble Methods for Noise Elimination in Classification Problems,” in Multiple Classifier Systems, vol. 2709 of Lecture Notes in Computer Science, pp. 317–325, Springer Berlin Heidelberg, Berlin, Heidelberg, 2003. View at Publisher · View at Google Scholar
  25. J. A. Sáez, J. Luengo, and F. Herrera, “Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure,” Neurocomputing, vol. 176, pp. 26–35, 2016. View at Publisher · View at Google Scholar
  26. E. T. Jaynes, “On The Rationale of Maximum-Entropy Methods,” Proceedings of the IEEE, vol. 70, no. 9, pp. 939–952, 1982. View at Publisher · View at Google Scholar · View at Scopus
  27. C. E. Shannon, “A mathematical theory of communication,” Bell Labs Technical Journal, vol. 27, pp. 379–423, 623--656, 1948. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. J. Abellán, “Uncertainty measures on probability intervals from the imprecise Dirichlet model,” International Journal of General Systems, vol. 35, no. 5, pp. 509–528, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  29. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, Mass, USA, 1984. View at MathSciNet
  30. M. Lichman, UCI Machine Learning Repository, 2013, http://archive.ics.uci.edu/ml.
  31. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2nd edition, 2005.
  32. J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research, vol. 7, pp. 1–30, 2006. View at Google Scholar · View at MathSciNet · View at Scopus
  33. J. Alcalá-Fdez, L. Sánchez, S. García et al., “KEEL: a software tool to assess evolutionary algorithms for data mining problems,” Soft Computing, vol. 13, no. 3, pp. 307–318, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Friedman, “The use of ranks to avoid the assumption of normality implicit in the analysis of variance,” Journal of the American Statistical Association, vol. 32, no. 200, pp. 675–701, 1937. View at Publisher · View at Google Scholar
  35. M. Friedman, “A comparison of alternative tests of significance for the problem of m rankings,” The Annals of Mathematical Statistics, vol. 11, no. 1, pp. 86–92, 1940. View at Publisher · View at Google Scholar · View at MathSciNet
  36. P. Nemenyi, Distribution-free multiple comparisons [Doctoral Dissertation], Princeton University, New Jersey, USA, 1963.
  37. J. A. Sáez, J. Luengo, and F. Herrera, “Fuzzy rule based classification systems versus crisp robust learners trained in presence of class noise's effects: A case of study,” in Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11, pp. 1229–1234, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus