Peng Nanbo; Zhang Yanxia; Zhao Yongheng
Source Publicationsciencechinaphysicsmechanicsastronomy
AbstractWe integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Peng Nanbo,Zhang Yanxia,Zhao Yongheng. asvmknnmethodforquasarstarclassification[J]. sciencechinaphysicsmechanicsastronomy,2013,56(6):1227.
APA Peng Nanbo,Zhang Yanxia,&Zhao Yongheng.(2013).asvmknnmethodforquasarstarclassification.sciencechinaphysicsmechanicsastronomy,56(6),1227.
MLA Peng Nanbo,et al."asvmknnmethodforquasarstarclassification".sciencechinaphysicsmechanicsastronomy 56.6(2013):1227.
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