基于多类支持向量机的恒星光谱分类方法  

Stellar Spectra Classification Method Based on Multi-Class Support Vector Machine

作　　者：张静[1] 刘忠宝[1] 宋文爱[1] 富丽贞[1] 章永来 ZHANG Jing;LIU Zhong-bao;SONG Wen-ai;FU Li-zhen;ZHANG Yong-lai(School of Software,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学软件学院,山西太原030051

出　　处：《光谱学与光谱分析》2018年第7期2307-2310,共4页Spectroscopy and Spectral Analysis

基　　金：山西省自然科学基金项目(201601D011042);山西省高等学校创新人才支持计划;中北大学杰出青年基金支持计划资助

摘　　要：支持向量机作为一种经典的分类方法被广泛应用于恒星光谱分类领域。该方法在实际应用中取得了较为理想的分类效果,但其面临无法解决多分类问题的挑战。在支持向量机的基础上,提出多类支持向量机,建立基于多类支持向量机的恒星光谱分类模型。该方法的最大优势是经过一次分类过程,可以确定多类样本的类属。SDSS DR8恒星光谱数据上的比较实验表明,本研究所提的方法较之已有多分类方法在分类性能上有一定的提升。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.

关 键 词：支持向量机 多类支持向量机 恒星光谱 自动分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]