[The application of adaptive boosting method in automated spectral classification of active galactic nuclei].
Given a set of low-redshift spectra of active galactic nuclei, the wave bands of spectra in the rest frame were intercepted according to the different features of emission lines of broad-line AGNs and narrow-line AGNs, and an adaptive boosting (Adaboost) method was developed to carry out the classification experiments of feature fusion. As a result, the wave band of Halpha and [N II] was confirmed to be the main discriminative feature between broad-line AGNs and narrow-line AGNs. Then based on the wave band of Halpha and [N II], the Adaboost method was used for the spectral classification. In this method, the "weak classifiers" were increased constantly during training until a scheduled error rate or a maximum cycle times was met, then the classification judgment of the consequent collective classifier was determined by the votes of respective judgments of these "weak classifiers". The Adaboost method needs not to adjust parameters in advance and the results of "weak classifiers" are only required to be better than random guessing, so its algorithm is very simple. As proved by the experiments, the adaboost method achieves good performance in the classification just based on the wave band of Halpha and [N II] so that it could be applied effectively to the automatic classification of large amount of AGN spectra from the large-scale spetral surveys.
Zhao
Mei-Fang
MF
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 10080, China. mfzhao@nlpr.ia.ac.cn
Luo
A-Li
AL
Wu
Fu-Chao
FC
Zhao
Yong-Heng
YH