In this paper we designed and implemented a new
ensemble of classifiers based on a sequence of classifiers which were
specialized in regions of the training dataset where errors of its
trained homologous are concentrated. In order to separate this
regions, and to determine the aptitude of each classifier to properly
respond to a new case, it was used another set of classifiers built
hierarchically. We explored a selection based variant to combine the
base classifiers. We validated this model with different base
classifiers using 37 training datasets. It was carried out a statistical
comparison of these models with the well known Bagging and
Boosting, obtaining significantly superior results with the
hierarchical ensemble using Multilayer Perceptron as base classifier.
Therefore, we demonstrated the efficacy of the proposed ensemble,
as well as its applicability to general problems.
[1] L. Breiman, Bagging predictors. Machine Learning, 1996. 24: p. 123-
140.
[2] Y. Freund and R. E. Schapire, Experiments with a new boosting
algorithm. Thirteenth International Conference on Machine Learning,
1996: p. 148-156.
[3] R. E. Schapire, The strength of weak learnability. Machine Learning,
1990. 5(2): p. 197-227.
[4] R. A. Jacobs, S. J. Nowlan, and G. E. Hinton, Adaptative mixtures of
local experts. Neural Computation, 1991. 3: p. 79-87.
[5] M. J. Jordan and R. A. Jacobs, Hirarchical mixtures of experts and the
EM algorithm. Neural Computation, 1994. 6: p. 79-87.
[6] D. Wolpert, Stacked generalization. Neural Networks, 1992. 5(2): p.
241-259.
[7] D. J. N. A. Asuncion. UCI Machine Learning Repository. 2007.
[8] C. Ferri, P. Flach, and J. Hernández-Orallo. Delagating Classifiers. in
21st International Conference on Machine Learning. 2004. Canada.
[1] L. Breiman, Bagging predictors. Machine Learning, 1996. 24: p. 123-
140.
[2] Y. Freund and R. E. Schapire, Experiments with a new boosting
algorithm. Thirteenth International Conference on Machine Learning,
1996: p. 148-156.
[3] R. E. Schapire, The strength of weak learnability. Machine Learning,
1990. 5(2): p. 197-227.
[4] R. A. Jacobs, S. J. Nowlan, and G. E. Hinton, Adaptative mixtures of
local experts. Neural Computation, 1991. 3: p. 79-87.
[5] M. J. Jordan and R. A. Jacobs, Hirarchical mixtures of experts and the
EM algorithm. Neural Computation, 1994. 6: p. 79-87.
[6] D. Wolpert, Stacked generalization. Neural Networks, 1992. 5(2): p.
241-259.
[7] D. J. N. A. Asuncion. UCI Machine Learning Repository. 2007.
[8] C. Ferri, P. Flach, and J. Hernández-Orallo. Delagating Classifiers. in
21st International Conference on Machine Learning. 2004. Canada.
@article{"International Journal of Information, Control and Computer Sciences:54805", author = "Abdel Rodríguez and Isis Bonet and Ricardo Grau and María M. García", title = "Judges System for Classifiers Specialization", abstract = "In this paper we designed and implemented a new
ensemble of classifiers based on a sequence of classifiers which were
specialized in regions of the training dataset where errors of its
trained homologous are concentrated. In order to separate this
regions, and to determine the aptitude of each classifier to properly
respond to a new case, it was used another set of classifiers built
hierarchically. We explored a selection based variant to combine the
base classifiers. We validated this model with different base
classifiers using 37 training datasets. It was carried out a statistical
comparison of these models with the well known Bagging and
Boosting, obtaining significantly superior results with the
hierarchical ensemble using Multilayer Perceptron as base classifier.
Therefore, we demonstrated the efficacy of the proposed ensemble,
as well as its applicability to general problems.", keywords = "classifiers, delegation, ensemble", volume = "1", number = "5", pages = "1273-4", }