自适应增强方法在光谱自动分类中的应用

针对活动星系核(AGN)光谱中发射线的不同特征，在恢复到静止系状态后的光谱上截取具有有效特征的波段范围，采用自适应增强(Adaboost)的方法，对宽线和窄线AGNs进行特征融合的分类实验，经分析，确定了以Hα和NⅡ]发射线为主的波段为宽线和窄线AGNs光谱的主要区别特征。再单独对Hα和NⅡ]发射线为主的波段，用自适应增强的方法对其进行光谱分类。自适应增强方法在训练过程中不断地加入“弱分类器”，直到达到某个预定的足够小的误差率或一定的循环次数，最后构成的总体分类器的分类判决由这些“弱分类器”各自的判决结果的投票来决定。此方法不需要事先调节参数，且“弱分类器”的分类结果只需好于随机猜测，算法简单。实验证明，对于单独采用以Hα和NⅡ]发射线为主的波段，自适应增强方法能达到较好的分类效果，从而可有效地应用于大型光谱巡天所产生的活动星系核光谱的自动分类中。

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.