Abstract：Support vector machine (SVM), a typical classification method, has been widely used in stellar spectra classification. It performs well in practice, while it encounters the multi-class classification challenge. In order to solve the problem above, multi-class support vector machine (MCSVM) was proposed in this paper based on the in-depth analysis of SVM. Meanwhile, the stellar spectra classification model based on multi-class support vector machine was constructed. The advantage of the proposed method is that the samples’ class can be determined by a classification process. Comparative experiments with the existed multi-class classification method on the SDSS DR8 datasets verify the effectiveness of the proposed method.
张 静，刘忠宝，宋文爱，富丽贞，章永来. 基于多类支持向量机的恒星光谱分类方法[J]. 光谱学与光谱分析, 2018, 38(07): 2307-2310.
ZHANG Jing, LIU Zhong-bao, SONG Wen-ai, FU Li-zhen, ZHANG Yong-lai. Stellar Spectra Classification Method Based on Multi-Class Support Vector Machine. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2307-2310.
[1] Xue J Q, Li Q B, Zhao Y H. Chinese Astronomy and Astrophysics, 2001, 25: 120.
[2] Alejandra R, Bernardino A, Carlos D, et al. Expert Systems with Applications, 2004, 27: 237.
[3] Malyuto V. New Astronomy, 2002, 7: 461.
[4] Bu Y D, Chen F Q, Pan J C. New Astronomy, 2014, 28: 35.
[5] Du C D, Luo A L, Yang H F. New Astronomy, 2017, 51: 51.
[6] ZHAO Mei-fang, WU Chao, LUO A-li, et al(赵梅芳, 吴 潮, 罗阿理, 等). Acta Astronomica Sinica(天文学报), 2007, 48(1): 1.
[7] SUN Shi-wei, LUO A-li, ZHANG Ji-fu(孙士卫, 罗阿理, 张继福). Astronomical Research & Technology, 2007, 4(3): 276.
[8] PAN Jing-chang, WANG Jie, JIANG Bin, et al(潘景昌, 王 杰, 姜 斌, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(8): 2651.
