Improving the k-NN method: rough set in edit training set

Caballero, Yailé et al · SEDICI UNLP · 2006

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Caballero, Yailé et al

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SEDICI UNLP

Year

2006

Language

English

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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. Applications in Artificial Intelligence - Learning and Neural Nets Red de Universidades con Carreras en Informática (RedUNCI)

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APA 7

Caballero, Y. E. A. (2006). Improving the k-NN method: rough set in edit training set. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/24150

MLA

Caballero, Yailé et al. Improving the k-NN method: rough set in edit training set. SEDICI UNLP, 2006. http://sedici.unlp.edu.ar/handle/10915/24150.

Chicago

Caballero, Yailé et al. 2006. Improving the k-NN method: rough set in edit training set. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/24150.

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Caballero, Y. E. A. 2006, Improving the k-NN method: rough set in edit training set, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/24150 [Accessed 10 Aug. 2026].

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Title: Improving the k-NN method: rough set in edit training set

Author / contributors: Caballero, Yailé et al

Publisher: SEDICI UNLP

Publication year: 2006

Language: English

Subjects

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Ciencias Informáticas; data analysis; training sets; Rough Set Theory (RST)