Abstract
Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST to improve the performance of k-NN method. The RST is used to edit and reduce 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 k-NN method using these techniques.
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Caballero, Y., Bello, R., Alvarez, D., Gareia, M.M., Pizano, Y. (2006). Improving the k-NN method: Rough Set in edit training set. In: Debenham, J. (eds) Professional Practice in Artificial Intelligence. IFIP WCC TC12 2006. IFIP International Federation for Information Processing, vol 218. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34749-3_3
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DOI: https://doi.org/10.1007/978-0-387-34749-3_3
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