Mapping of nearest neighbor for classification

N. Ishii, Ippei Torii, Y. Bao, Hidekazu Tanaka
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引用次数: 6

Abstract

Dimension reduction of data is an important theme in the data processing and on the web to represent and manipulate higher dimensional data. Reduct in the rough set is a minimal subset of features, which has almost the same discernible power as the entire features in the higher dimensional scheme. But, there are problems in the application of reducts for classification. Here, we develop a method which connects reducts and the nearest neighbor method to classify data with higher classification accuracy. To improve the classification ability of reducts, we develop a new graph mapping method of the nearest neighbor based on reducts and weighted modified reducts for the classification with higher accuracy. Then, the mapping method is useful and the weighted modified reduct classifies with higher accuracy.
用于分类的最近邻映射
数据降维是数据处理和网络上表示和处理高维数据的一个重要主题。粗糙集中的约简是特征的最小子集,它与高维格式中的整个特征具有几乎相同的可识别能力。但是,在应用约简进行分类时存在一些问题。在这里,我们开发了一种将约简和最近邻方法相结合的方法,以获得更高的分类精度。为了提高约简的分类能力,我们提出了一种基于约简和加权修正约简的最近邻图映射方法,以获得更高的分类精度。因此,映射方法是有用的,加权修正约简分类精度更高。
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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