1Department of Computer Science. University of Camaguey, Cuba.
2Department of Computer Science. Universidad Central de Las Villas, Cuba.
*E-mail: yailec@yahoo.com
**E-mail: rbellop@uclv.edu.cu
Rough Set Theory (RST) is a technique for data analysis. In this paper, we use RST to improve the performance of the k-NN method and the MLP neural network. The RST is used to edit the training set. We propose two methods to edit training sets, which are based on the lower and upper approximations. Experimental results show a satisfactory performance of the k-NN method and MLP using these techniques.
k-NN method, MLP, Rough Set Theory, data analysis, edit training set
