%0 Journal Article
%T La Teor¨ªa de los Conjuntos Aproximados y las T¨¦cnicas de Boostrap para la Edici¨®n de Conjuntos de Entrenamiento. Su Aplicaci¨®n en el Pron¨®stico Meteorol¨®gico.
%A Beitmantt C¨¢rdenas
%A Yail¨¦ Caballero
%A Rafael Bello
%J Avances en Sistemas e Inform¨¢tica
%D 2007
%I 
%X Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST and boostrap¡¯s technnique to improve the performance of classifiers. The RST is used to edit and reduce the training set. We propose a method to edit training sets, which is based on the lower and upper approximations and boostrap¡¯s technique. The accelerated growth of the environmental of information volumes on processes, phenomena and reports brings about an increasing interest in the possibility of discovering knowledge from data sets. Experimental results show a satisfactory performance using these techniques.
%K Boostrap¡¯s Technique
%K Edit to Training Set
%K Rough Set Theory
%K Weather forecasting
%U http://pisis.unalmed.edu.co/avances/archivos/ediciones/Edicion%20Avances%202007%203/18.pdf