从海量恒星光谱中发现稀有光谱是天文学研究的重要课题之一。 与一般光谱相比, 稀有光谱数量较少, 因此, 传统分类方法无法正常工作。 究其原因是这些方法不仅在分类决策时并未对稀有光谱予以更多关注, 而且只关注分类的准确率。 鉴于此, 在总结当前分类方法的基础上, 深入分析互信息与决策树之间的关系, 提出基于互信息的代价缺失决策树。 SDSS DR8中K型、F型、G型以及M型恒星光谱上的比较实验表明, 与传统分类方法相比, 所提方法能够较好地完成稀有光谱识别的任务。

Distinguishing the rare spectra from the majority of stellar spectra is one of quite important issues in astronomy. As the size of the rare spectra is much smaller than the majority of the spectra, many traditional classifiers can’t work effectively because they only focus on the classification accuracy and have not paid enough attentions on the rare spectra. In view of this, the relationship between the decision tree and mutual information is discussed on the basis of summarizing the traditional classifiers, and the cost-free decision tree based on mutual information is proposed in this paper to improve the performance of distinguishing the rare spectra. In the experiment, we investigate the performance of the proposed method on the K-type, F-type, G-type, M-type datasets from Sloan Digital Sky Survey (SDSS), Data Release 8. It can be concluded that the proposed method can complete the rare spectra distinguishing task compared with several traditional classifiers.