Abstract
In this paper we consider the application of Bagging to Projection Pursuit Regression and we study the impact of this technique on the reduction of prediction error. Using artificial and real-data sets, we investigate the predictive performance of this method with respect to the number of aggregated predictors, the number of functions in the single Projection Pursuit model and the signal-to-noise ratio of the sample data.
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© 2001 Springer-Verlag Berlin Heidelberg
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Borra, S., Di Ciaccio, A. (2001). Reduction of Prediction Error by Bagging Projection Pursuit Regression. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_30
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DOI: https://doi.org/10.1007/978-3-642-59471-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41488-9
Online ISBN: 978-3-642-59471-7
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