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La Teoría de los Conjuntos Aproximados y las Técnicas de Boostrap para la Edición de Conjuntos de Entrenamiento. Su Aplicación en el Pronóstico Meteorológico.

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Author(s): Beitmantt Cárdenas | Yailé Caballero | Rafael Bello

Journal: Avances en Sistemas e Informática
ISSN 1657-7663

Volume: 4;
Issue: 3;
Start page: 165;
Date: 2007;
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Keywords: Boostrap’s Technique | Edit to Training Set | Rough Set Theory | Weather forecasting

ABSTRACT
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.
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