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5th International Conference on Intelligent Systems Design and Applications (ISDA'05)
Using rough sets to edit training set in k-NN method
Wroclaw, Poland
September 08-September 10
ISBN: 0-7695-2286-6
ASCII Text
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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
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@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
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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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