用粗糙集编辑k-NN方法中的训练集

5th International Conference on Intelligent Systems Design and Applications (ISDA'05) Pub Date : 2005-09-08 DOI:10.1109/ISDA.2005.98

Y. Mota, S. Joseph, Yuniesky Lezcano, Rafael Bello, M. Lorenzo, Yaimara Pizano

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引用次数: 7

摘要

粗糙集理论(RST)是一种数据分析技术。在本文中，我们使用RST来改进k-NN方法的性能。RST用于编辑训练集。我们提出了基于上下近似的两种训练集编辑方法。实验结果表明，使用这些技术的k-NN具有令人满意的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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