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.
Borra, S., Di Ciaccio, A. (2005). Methods to compare nonparametric classifiers and to select the predictors. In New developments in classification and data analysis: proceedings of the meeting of the Classification and data analysis group (CLADAG) of the Italian statistical society, University of Bologna, September 22-24, 2003. Edited by Vichi, M., Monari, P., Mignani, S., Montanari, A (pp.11-19). Berlin : Springer [10.1007/b138989].
Methods to compare nonparametric classifiers and to select the predictors
BORRA, SIMONE;
2005-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
