Using rough sets to edit training set in k-NN method

Wroclaw, Poland September 08-September 10

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http://doi.ieeecomputersociety.org/10.1109/ISDA.2005.98

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	Yaile Caballero, Simone Joseph, Yuniesky Lezcano, Rafael Bello, Maria M Garcia, Yaimara Pizano, "Using rough sets to edit training set in k-NN method," Intelligent Systems Design and Applications, International Conference on, pp. 456-463, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), 2005.

	BibTex	x
	@article{ 10.1109/ISDA.2005.98, author = {Yaile Caballero and Simone Joseph and Yuniesky Lezcano and Rafael Bello and Maria M Garcia and Yaimara Pizano}, title = {Using rough sets to edit training set in k-NN method}, journal ={Intelligent Systems Design and Applications, International Conference on}, volume = {0}, year = {2005}, isbn = {0-7695-2286-6}, pages = {456-463}, doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2005.98}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Intelligent Systems Design and Applications, International Conference on TI - Using rough sets to edit training set in k-NN method SN - 0-7695-2286-6 SP456 EP463 A1 - Yaile Caballero, A1 - Simone Joseph, A1 - Yuniesky Lezcano, A1 - Rafael Bello, A1 - Maria M Garcia, A1 - Yaimara Pizano, PY - 2005 KW - k-NN method KW - Rough Set Theory KW - data analysis VL - 0 JA - Intelligent Systems Design and Applications, International Conference on ER -

Yaile Caballero, Universidad de Camaguey, Cuba

Simone Joseph, Universidad de Camaguey, Cuba

Yuniesky Lezcano, Universidad de Camaguey, Cuba

Rafael Bello, Universidad Central de Las Villas, Cuba

Maria M Garcia, Universidad Central de Las Villas, Cuba

Yaimara Pizano, Universidad de Camaguey, Cuba

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. 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 using these techniques.

Index Terms:

k-NN method, Rough Set Theory,data analysis

Citation:

Yaile Caballero, Simone Joseph, Yuniesky Lezcano, Rafael Bello, Maria M Garcia, Yaimara Pizano, "Using rough sets to edit training set in k-NN method," isda, pp.456-463, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), 2005

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