Abstract
In the framework of Generalized Additive Models (GAM) an automatic data-driven procedure is introduced for assigning an appropriate smoother to each covariate and for defining an ordering entrance for the covariates in the model. The resulting Smoothing Score algorithm aims to improve model indentifiability. It uses the bagging procedure in order to select the smoothers to be assigned to each covariate and a new scoring measure able to rank the candidate smoothers with respect to their bagged predictive accuracy. The adequacy of this scoring measure is evaluated on artificial data. A comparison between the smoothing score algorithm and the standard GAM is made using real data concerning a classification task.
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References
BREIMAN, L. (1996). Bagging predictors, Machine Learning, 26, 46–59.
BLAKE, C.L. and MERTZ, C.J. (1998). UCI repository of machine learning databases, http://www.ics.uci.edu .
CONVERSANO, C. (2002). Bagged Mixtures of Classifiers using Model Scoring Criteria, Journal of Pattern Analysis and Applications, 5, 4, 351–362.
CONVERSANO, C. (2001). Generalized Additive Multi-Mixture Models: Scoring Models and Predictors, In: Klein, B. and Korsholm, L. (eds.), New Trends in Statistical Modelling, Proceedings of the 16th International Workshop on Statistical Modelling, University of Southern Denmark, 103–110.
CONVERSANO, C, SICILIANO, R. and MOLA, F. (2002). Generalized Additive Multi-Mixture Models for Data Mining, Computational Statistics and Data Analysis, 38, 4, 487–500, special issue on Nonlinear Methods and Data Mining.
HASTIE, T., FRIEDMAN, J. and TIBSHIRANI, R. (2001). Elements of Statistical Learning, Springer, New York.
HASTIE, T. and TIBSHIRANI, R. (1990). Generalized Additive Models, Chapman & Hall, London.
HASTIE, T.(1992). Generalized Additive Models, In: Chambers, J. M. and Hastie, T. (eds.): Statistical Models in S, Wadsworth & Brooks/Cole Computer Science Series, Pacific Groove, California.
SCHIMEK, M.G. (2000). Additive and Generalized Additive Models, In: Schimek M.G. (Ed.), Smoothing and Regression, John Wiley & Sons, New York.
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© 2004 Springer-Verlag Berlin Heidelberg
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Conversano, C. (2004). Smoothing Score Algorithm for Generalized Additive Models. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_8
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DOI: https://doi.org/10.1007/978-3-642-17111-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20889-1
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