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Stellar Spectra Classification with Entropy-Based Learning Machine
Liu Zhong-bao1; Ren Juan-juan2; Song Wen-ai1; Zhang Jing1; Kong Xiao2; Fu Li-zhen1
2018-02-01
Source PublicationSPECTROSCOPY AND SPECTRAL ANALYSIS
Volume38Issue:2Pages:660-664
AbstractData mining are widely used in the stellar spectra classification. In order to improve the efficiencies of traditional spectra classification methods, Entropy-based Learning Machine (ELM) was proposed in this paper. The entropy was used to describe the uncertainty of classification in ELM. In order to obtain the desired classification efficiencies, the classification uncertainty should be minimized, based on which, we can obtain the optimization problem of ELM. It can be verified that ELM performs well in the binary classification and in the rare spectra mining. Several comparative experiments on the 4 subclasses of K-type spectra, 3 subclasses of F-type spectra and 3 subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS) verified that ELM performs better than kNN (k Nearest Neighbor) and SVM (Support Vector Machine) in dealing with the problem of stellar spectra classification on the SDSS datasets.
SubtypeArticle
KeywordData Mining Stellar Spectra Classification Entropy Sloan Digital Sky Survey (Sdss)
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNature Science Foundation of Shanx(201601D011042) ; Nature Science Foundation of Shanx(201601D011042) ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China ; Nature Science Foundation of Shanx(201601D011042) ; Nature Science Foundation of Shanx(201601D011042) ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China
DOI10.3964/j.issn.1000-0593(2018)02-0660-05
Indexed BySCI
Language英语
Funding OrganizationNature Science Foundation of Shanx(201601D011042) ; Nature Science Foundation of Shanx(201601D011042) ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China ; Nature Science Foundation of Shanx(201601D011042) ; Nature Science Foundation of Shanx(201601D011042) ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China ; Program for the Outstanding Innovative Team of High Learning Learning Instituttions of Shanxi, Outstanding Youth Funds of North University of China
WOS Research AreaSpectroscopy
WOS SubjectSpectroscopy
WOS IDWOS:000426142100054
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/20302
Collection光学天文研究部
Affiliation1.North Univ China, Sch Software, Taiyuan 030051, Shanxi, Peoples R China
2.Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Liu Zhong-bao,Ren Juan-juan,Song Wen-ai,et al. Stellar Spectra Classification with Entropy-Based Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2018,38(2):660-664.
APA Liu Zhong-bao,Ren Juan-juan,Song Wen-ai,Zhang Jing,Kong Xiao,&Fu Li-zhen.(2018).Stellar Spectra Classification with Entropy-Based Learning Machine.SPECTROSCOPY AND SPECTRAL ANALYSIS,38(2),660-664.
MLA Liu Zhong-bao,et al."Stellar Spectra Classification with Entropy-Based Learning Machine".SPECTROSCOPY AND SPECTRAL ANALYSIS 38.2(2018):660-664.
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