Abstract
The goal of combining the outputs of multiple models is to form an improved meta-model with higher generalization capability than the best single model used in isolation. Most popular ensemble methods do specify neither the number of component models nor their complexity. However, these parameters strongly influence the generalization capability of the meta-model. In this paper we propose an ensemble method which generates a meta-model with optimal values for these parameters. The proposed method suggests using resampling techniques to generate multiple estimations of the generalization error and multiple comparison procedures to select the models that will be combined to form the meta-model. Experimental results show the performance of the model on regression and classification tasks using artificial and real databases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Neural network for pattern recognition. Clarendon Press-Oxford (1995)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine. Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Dietterich, T.G.: Machine Learning Research: Four Current Directions. Artificial Intelligence Magazine 18(4), 97–136 (1997)
Don Lehmkuhl, L.: Nonparametric statistics: methods for analyzing data not meeting assumptions required for the application of parametric tests. Journal of prosthetics and orthotics 8(3), 105–113 (1996)
Guerrero, E., Yáñez, A., Galindo, P., Pizarro, J.: Repeated measures multiple comparison procedures applied to model selection in neural networks. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 88–95. Springer, Heidelberg (2001)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptative mixtures of local experts. Neural Computation 3(1), 79–87 (1991)
Jutten, C., et al.: ESPIRIT basic research project number 689 ELENA, ftp.dice.ucl.ac.be/pub/neural-net/ELENA/databases
Krogh, A., Vedelsby, J.: Neural networks ensembles, cross validation and active learning. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 231–238. The MIT Press, Cambridge (1995)
Lasarev, M.R.: Methods for p-value adjustment, Oregon Health & Science University (2001), http://medir.ohsu.edu/~geneview/education/dec19_h.pdf
Optiz, D.W., Shavlik, J.W.: Generating accurate and diverse members of a neural-network ensemble. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 535–541. The MIT Press, Cambridge (1996)
Pace, R.K., Barry, R.: Sparse Spatial Autoregressions. Statistics and Probability Letters 33, 291–297 (1997), http://lib.stat.cmu.edu/
Perrone, M.P., Cooper, L.N.: When networks disagree: ensemble method for neural networks. In: Mammone, R.J. (ed.) Artificial Neural Networks for Speech and Vision, pp. 126–142. Chapman & Hall, New York (1993)
Pizarro, J., Guerrero, E., Galindo, P.: Multiple comparison procedures applied to model selection. Neurocomputing 48, 152–159 (2001)
Sarle, W.: Donoho-Johnstone benchmarks: neural nets results (1999), ftp://ftp.sas.com/pub/neural/dojo/dojo.html
Scharkey, A.J.C.: On Combining Artificial Neural Nets. Connection Science 8(3/4), 299–314 (1996)
Yáñez, A.: Regresión mediante la combinación de modelos seleccionados mediante técnicas de remuestreo y procedimientos de comparación múltiple. Thesis. University of Cádiz
Zar, J.H.: Biostatistical analysis. Prentice-Hall, Englewood Cliffs (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Escolano, A.Y., Riaño, P.G., Junquera, J.P., Vázquez, E.G. (2005). Statistical Ensemble Method (SEM): A New Meta-machine Learning Approach Based on Statistical Techniques. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_24
Download citation
DOI: https://doi.org/10.1007/11494669_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)