Boosting recombined weak classifiers

Pattern Recognition Letters

Volumn 29, Issue 8, 2008, Pages 1049-1059

  Rodríguez, Juan J ^a   Maudes, Jesús ^a  

^a UNIVERSITY OF BURGOS   (Spain)

Author keywords

Boosting; Classifier ensembles; Decision stumps

Indexed keywords

COMPUTATIONAL COMPLEXITY; DECISION TREES; FEATURE EXTRACTION; PROBLEM SOLVING; STATISTICAL METHODS;

BOOSTING; CLASSIFIER ENSEMBLES; DECISION STUMPS; WEAK CLASSIFIERS;

LEARNING ALGORITHMS;

EID: 42049117782     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2007.06.019     Document Type: Article

References (30)

1. Blake, C.L., Merz, C.J., 1998. UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html.
2. Breiman L. Bagging predictors. Mach. Learn. 24 2 (1996) 123-140
3. Breiman L. Random forests. Mach. Learn. 45 1 (2001) 5-32
4. Buntine W. Learning classification trees. Artificial Intelligence Frontiers in Statistics (1993), Chapman and Hall, London 182-201
5. Caruana, R., Niculescu-Mizil, A., 2006. An empirical comparison of supervised learning algorithms. In: ICML 2006: 23rd International Conference on Machine Learning.
6. Demšar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006) 1-30
7. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Mach. Learn. 40 2 (2000) 139-158
8. Eibl G., and Pfeiffer K.P. Multiclass-boosting for weak classifiers. J. Mach. Learn. Res. 6 (2005) 189-210
9. Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M., 2003. Beam search extraction and forgetting strategies on shared ensembles. In: Multiple Classifier Systems, Fourth International Workshop, MCS 2003.
10. Freund Y., and Scapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 1 (1997) 119-139
11. Friedman J., Hastie T., and Tibshirani R. Additive logistic regression: A statistical view of boosting. Ann. Stat. 38 2 (2000) 337-374
12. Guruswami, V., Sahai, A., 1999. Multiclass learning, boosting, and error-correcting codes. In: Proceedings of the 12th Annual Conference on Computational Learning Theory (COLT 1999), ACM.
13. Ho T.K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20 8 (1998) 832-844
14. Kohavi, R., Kunz, C., 1997. Option decision trees with majority votes. In: Machine Learning Proceedings of the 14th International Conference.
15. Kuncheva L.I. Combining Pattern Classifiers: Methods and Algorithms (2004), Wiley-Interscience
16. Kuncheva L. Diversity in multiple classifier systems (editorial). Inform. Fusion 6 1 (2005) 3-4
17. Li, L., 2006. Multiclass boosting with repartitioning. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006.
18. Li, J., Liu, H., 2003. Ensembles of cascading trees. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM'03), pp. 585-588.
19. Nadeau C., and Bengio Y. Inference for the generalization error. Mach. Learn. 52 (2003) 239-281
20. Nock, R., Nielsen, F., 2006. A Real generalization of discrete adaboost. In: Brewka, G., Coradeschi, S., Perini, A., Traverso, P. (Eds.), ECAI-06, Proceedings of the 17th European Conference on Artificial Intelligence, vol. 141 of Frontiers in Artificial Intelligence and Applications. IOS Press.
21. Reyzin, L., Schapire, R.E., 2006. How boosting the margin can also boost classifier complexity. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006.
22. Rifkin R., and Klautau A. In defense of one-vs-all classification. J. Mach. Learn. Res. 5 (2004) 101-141
23. Rodríguez, J.J., Maudes, J., 2006. Ensembles of grafted trees. In: Brewka, G., Coradeschi, S., Perini, A., Traverso, P. (Eds.), ECAI-06, Proceedings of the 17th European Conference on Artificial Intelligence, vol. 141 of Frontiers in Artificial Intelligence and Applications. IOS Press, pp. 803-804.
24. Rodríguez J.J., Kuncheva L.I., and Alonso C.J. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28 10 (2006) 1619-1630. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.2%11
25. Schapire R.E. The strength of weak learnability. Mach. Learn. 5 2 (1990) 197-227
26. Schapire, R.E., 1997. Using output codes to boost multiclass learning problems. In: Proceedings of the 14th International Conference on Machine Learning (ICML-97). pp. 313-321.
27. Schapire, R.E., 1999. A brief introduction to boosting. In: Dean, T. (Ed.), Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99). Morgan Kaufmann, pp. 1401-1406.
28. Schapire, R.E., 2002. The boosting approach to machine learning: An overview. In: MSRI Workshop on Nonlinear Estimation and Classification. http://www.cs.princeton.edu/schapire/papers/msri.ps.gz.
29. Schapire, R.E., Singer, Y., 1998. Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT 1998), ACM. pp. 80-91.
30. Wolpert D. Stacked generalization. Neural Network 5 2 (1992) 241-260