2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2006)
Sept. 26, 2006 to Sept. 29, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SYNASC.2006.6
Yaile Caballero , Universidad de Camaguey, Cuba
Rafael Bello , Universidad Central de Las Villas, Cuba
Alberto Taboada , Universidad Central de Las Villas, Cuba
Ann Nowe , Vrije Universiteit it Brussel, Belgium
Maria M. Garcia , Universidad Central de Las Villas, Cuba
Gladys Casas , Universidad Central de Las Villas, Cuba
Due to the wide availability of huge amounts of data in electronic forms, the necessity of turning such data into useful knowledge has increased. This is a proposal of learning from examples. In this paper, we propose measures to evaluate the quality of training sets used by algorithms for learning classification. Our training set assessment relies on measures provided by Rough Sets Theory. Our experimental results involved three classifiers (k-NN, C-4.5 and MLP) applied to international data bases. The new measure we propose shows good results on these test cases.
G. Casas, Y. Caballero, R. Bello, A. Taboada, A. Nowe and M. M. Garcia, "A New Measure Based in the Rough Set Theory to Estimate the Training Set Quality," 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing(SYNASC), Timisoara, Romania, 2006, pp. 133-140.