k-Nearest Neighbors for automated classification of celestial objects  

作　　者：LI LiLi1,2,3, ZHANG YanXia1 & ZHAO YongHeng1 1 National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China 2 Department of Physics, Hebei Normal University, Shijiazhuang 050016, China 3 Weishanlu Middle School, Tianjin 300222, China 

出　　处：《Science China(Physics,Mechanics & Astronomy)》2008年第7期916-922,共7页中国科学：物理学、力学、天文学（英文版）

基　　金：the National Natural Science Foundation of China (Grant Nos. 10473013, 10778724 and 90412016)

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).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).

关 键 词：k-Nearest NEIGHBORS DATA analysis CLASSIFICATION astronomical CATALOGUES 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]