LiLi Li; YanXia Zhang; YongHeng Zhao
Source Publicationscienceinchinaseriesgphysicsmechanicsastronomy
AbstractThe 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).
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
LiLi Li,YanXia Zhang,YongHeng Zhao. knearestneighborsforautomatedclassificationofcelestialobjects[J]. scienceinchinaseriesgphysicsmechanicsastronomy,2008,51(7):916.
APA LiLi Li,YanXia Zhang,&YongHeng Zhao.(2008).knearestneighborsforautomatedclassificationofcelestialobjects.scienceinchinaseriesgphysicsmechanicsastronomy,51(7),916.
MLA LiLi Li,et al."knearestneighborsforautomatedclassificationofcelestialobjects".scienceinchinaseriesgphysicsmechanicsastronomy 51.7(2008):916.
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