k-Nearest Neighbors for automated classification of celestial objects

被引：0
作者：
LiLi Li
YanXia Zhang
YongHeng Zhao
机构：
[1] Chinese Academy of Sciences,National Astronomical Observatories
[2] Hebei Normal University,Department of Physics
[3] Weishanlu Middle School,undefined
来源：
Science in China Series G: Physics, Mechanics and Astronomy | 2008年 / 51卷
关键词：
-Nearest Neighbors; data analysis; classification; astronomical catalogues;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).
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页码：916 / 922
页数：6
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