Abstract
In this paper we examine some nonparametric evaluation methods to compare the prediction capability of supervised classification models. We show also the importance, in nonparametric models, to eliminate the noise variables with a simple selection procedure. It is shown that a simpler model usually gives lower prediction error and is more interpretable. We show some empirical results applying nonparametric classification models on real and artificial data sets.
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References
BREIMAN, L. (2001): Looking inside the black box, Wald Lecture 2, Berkeley University.
HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J. (2001): The elements of statistical learning. Springer Series in Statistics. Springer-Verlag, New York.
KOHAVI, R. (1995): A study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, in Proceedings of the 14th IJCAI, CA:Morgan Kaufmann, San Francisco, 338–345.
SCHAPIRE, R.E., FREUND, Y., BARTLETT, P., LEE, W.S. (1998): Boosting the Margin: A new explanation for the effectiveness of voting methods, The Annals of Statistics, 26, 1651–1686.
YAO, Y.Y., WONG, S.K.M., BUTZ, C.J. (1999): On Information-Theoretic Measures of Attribute Importance, proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 133–137.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Borra, S., Di Ciaccio, A. (2005). Methods to Compare Nonparametric Classifiers and to Select the Predictors. In: Bock, HH., et al. New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27373-5_2
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DOI: https://doi.org/10.1007/3-540-27373-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23809-6
Online ISBN: 978-3-540-27373-8
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