Abstract
This work presents an experimental study of ensemble methods for regression, using Multilayer Perceptrons (MLP) as the base method and 61 datasets. The considered ensemble methods are Randomization, Random Subspaces, Bagging, Iterated Bagging and AdaBoost.R2. Surprisingly, because it is in contradiction to previous studies, the best overall results are for Bagging. The cause of this difference can be the base methods, MLP instead of regression or model trees. Diversity-error diagrams are used to analyze the behaviour of the ensemble methods. Compared to Bagging, the additional diversity obtained with other methods do not compensate the increase in the errors of the ensemble members.
This work was supported by the Project 2009/00204/001 of “Caja de Burgos” and University of Burgos and the Project TIN2008-03151 of the Spanish Ministry of Education and Science.
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Pardo, C., Rodríguez, J.J., García-Osorio, C., Maudes, J. (2010). An Empirical Study of Multilayer Perceptron Ensembles for Regression Tasks. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_12
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