Distinguishing the Rare Spectra with the Unbalanced Classification Method Based on Mutual Information
LIU Zhong-bao1*, REN Juan-juan2, KONG Xiao3
1. School of Computer and Control Engineering, North University of China, Taiyuan 030051, China 2. Key Laboratory of Optical Astronomy, NAOC, Chinese Academy of Sciences, Beijing 100012, China 3. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
Abstract：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.
Key words：Unbalanced classification;Mutual information;Rare spectra;Decision tree
刘忠宝1*，任娟娟2，孔 啸3 . 利用基于互信息的不平衡分类方法识别稀有光谱 [J]. 光谱学与光谱分析, 2016, 36(11): 3746-3751.
LIU Zhong-bao1*, REN Juan-juan2, KONG Xiao3 . Distinguishing the Rare Spectra with the Unbalanced Classification Method Based on Mutual Information. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(11): 3746-3751.
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