Comparison of Several Algorithms for Celestial object Classification
Identifieur interne :
000124 ( PascalFrancis/Corpus );
précédent :
000123;
suivant :
000125
Comparison of Several Algorithms for Celestial object Classification
Auteurs : NANBO PENG ;
YANXIA ZHANG ;
YONGHENG ZHAOSource :
-
Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.
RBID : Pascal:11-0004539
Descripteurs français
English descriptors
Abstract
We present a comparative study of implementation of supervised classification algorithms on classification of celestial objects. Three different algorithms including Linear Discriminant Analysis (LDA), K-Dimensional Tree (KD-tree), Support Vector Machines (SVMs) are used for classification of pointed sources from the Sloan Digital Sky Survey (SDSS) Data Release Seven. All of them have been applied and tested on the SDSS photometric data which are filtered by stringent conditions to make them play the best performance. Each of six performance metrics of SVMs can achieve very high performance (99.00%). The performances of KD-tree are also very good since six metrics are over 97.00%. Although five metrics are more than 90.00%, the performances of LDA are relatively poor because the accuracy of positive prediction only reaches 85.98%. Moreover, we discuss what input pattern is the best combination of different parameters for the effectiveness of these methods, respectively.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A06 | | | | @3 p. 2 |
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| A08 | 01 | 1 | ENG | @1 Comparison of Several Algorithms for Celestial object Classification |
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| A09 | 01 | 1 | ENG | @1 Software and cyberinfrastructure for astronomy : 27-30 June 2010, San Diego, California, United States |
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| A11 | 01 | 1 | | @1 NANBO PENG |
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| A11 | 02 | 1 | | @1 YANXIA ZHANG |
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| A11 | 03 | 1 | | @1 YONGHENG ZHAO |
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| A12 | 01 | 1 | | @1 RADZIWILL (Nicole M.) @9 ed. |
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| A12 | 02 | 1 | | @1 BRIDGER (Alan) @9 ed. |
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| A14 | 01 | | | @1 Key Laboratory of Optical Astronomy, National Astronomical Observatories; Chinese Academy of Sciences @2 Beijing 100012 @3 CHN @Z 1 aut. @Z 2 aut. @Z 3 aut. |
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| A44 | | | | @0 0000 @1 © 2011 INIST-CNRS. All rights reserved. |
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| C01 | 01 | | ENG | @0 We present a comparative study of implementation of supervised classification algorithms on classification of celestial objects. Three different algorithms including Linear Discriminant Analysis (LDA), K-Dimensional Tree (KD-tree), Support Vector Machines (SVMs) are used for classification of pointed sources from the Sloan Digital Sky Survey (SDSS) Data Release Seven. All of them have been applied and tested on the SDSS photometric data which are filtered by stringent conditions to make them play the best performance. Each of six performance metrics of SVMs can achieve very high performance (99.00%). The performances of KD-tree are also very good since six metrics are over 97.00%. Although five metrics are more than 90.00%, the performances of LDA are relatively poor because the accuracy of positive prediction only reaches 85.98%. Moreover, we discuss what input pattern is the best combination of different parameters for the effectiveness of these methods, respectively. |
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| C03 | 02 | X | ENG | @0 High performance @5 07 |
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| C03 | 02 | X | SPA | @0 Alto rendimiento @5 07 |
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| C03 | 03 | X | FRE | @0 Classification supervisée @5 18 |
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| C03 | 03 | X | ENG | @0 Supervised classification @5 18 |
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| C03 | 03 | X | SPA | @0 Clasificación supervisada @5 18 |
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| C03 | 04 | X | FRE | @0 Source ponctuelle @5 19 |
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| C03 | 04 | X | ENG | @0 Point source @5 19 |
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| C03 | 05 | X | ENG | @0 Sky @5 20 |
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Format Inist (serveur)
| NO : | PASCAL 11-0004539 INIST |
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| ET : | Comparison of Several Algorithms for Celestial object Classification |
|---|
| AU : | NANBO PENG; YANXIA ZHANG; YONGHENG ZHAO; RADZIWILL (Nicole M.); BRIDGER (Alan) |
|---|
| AF : | Key Laboratory of Optical Astronomy, National Astronomical Observatories; Chinese Academy of Sciences/Beijing 100012/Chine (1 aut., 2 aut., 3 aut.) |
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| DT : | Publication en série; Congrès; Niveau analytique |
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| SO : | Proceedings of SPIE, the International Society for Optical Engineering; ISSN 0277-786X; Coden PSISDG; Etats-Unis; Da. 2010; Vol. 7740; No. p. 2; 77402M.1-77402M.10; Bibl. 22 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | We present a comparative study of implementation of supervised classification algorithms on classification of celestial objects. Three different algorithms including Linear Discriminant Analysis (LDA), K-Dimensional Tree (KD-tree), Support Vector Machines (SVMs) are used for classification of pointed sources from the Sloan Digital Sky Survey (SDSS) Data Release Seven. All of them have been applied and tested on the SDSS photometric data which are filtered by stringent conditions to make them play the best performance. Each of six performance metrics of SVMs can achieve very high performance (99.00%). The performances of KD-tree are also very good since six metrics are over 97.00%. Although five metrics are more than 90.00%, the performances of LDA are relatively poor because the accuracy of positive prediction only reaches 85.98%. Moreover, we discuss what input pattern is the best combination of different parameters for the effectiveness of these methods, respectively. |
|---|
| CC : | 001D02B07B; 001D02A06 |
|---|
| FD : | Analyse discriminante; Haute performance; Classification supervisée; Source ponctuelle; Ciel; Photométrie; Métrique; Théorie graphe; Analyse statistique; Classification à vaste marge |
|---|
| ED : | Discriminant analysis; High performance; Supervised classification; Point source; Sky; Photometry; Metric; Graph theory; Statistical analysis; Vector support machine |
|---|
| SD : | Análisis discriminante; Alto rendimiento; Clasificación supervisada; Fuente puntual; Cielo; Fotometría; Métrico; Teoría grafo; Análisis estadístico; Máquina ejemplo soporte |
|---|
| LO : | INIST-21760.354000174702880860 |
|---|
| ID : | 11-0004539 |
|---|
Links to Exploration step
Pascal:11-0004539
Le document en format XML
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</fA30><server><NO>PASCAL 11-0004539 INIST</NO>
<ET>Comparison of Several Algorithms for Celestial object Classification</ET>
<AU>NANBO PENG; YANXIA ZHANG; YONGHENG ZHAO; RADZIWILL (Nicole M.); BRIDGER (Alan)</AU>
<AF>Key Laboratory of Optical Astronomy, National Astronomical Observatories; Chinese Academy of Sciences/Beijing 100012/Chine (1 aut., 2 aut., 3 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Proceedings of SPIE, the International Society for Optical Engineering; ISSN 0277-786X; Coden PSISDG; Etats-Unis; Da. 2010; Vol. 7740; No. p. 2; 77402M.1-77402M.10; Bibl. 22 ref.</SO>
<LA>Anglais</LA>
<EA>We present a comparative study of implementation of supervised classification algorithms on classification of celestial objects. Three different algorithms including Linear Discriminant Analysis (LDA), K-Dimensional Tree (KD-tree), Support Vector Machines (SVMs) are used for classification of pointed sources from the Sloan Digital Sky Survey (SDSS) Data Release Seven. All of them have been applied and tested on the SDSS photometric data which are filtered by stringent conditions to make them play the best performance. Each of six performance metrics of SVMs can achieve very high performance (99.00%). The performances of KD-tree are also very good since six metrics are over 97.00%. Although five metrics are more than 90.00%, the performances of LDA are relatively poor because the accuracy of positive prediction only reaches 85.98%. Moreover, we discuss what input pattern is the best combination of different parameters for the effectiveness of these methods, respectively.</EA>
<CC>001D02B07B; 001D02A06</CC>
<FD>Analyse discriminante; Haute performance; Classification supervisée; Source ponctuelle; Ciel; Photométrie; Métrique; Théorie graphe; Analyse statistique; Classification à vaste marge</FD>
<ED>Discriminant analysis; High performance; Supervised classification; Point source; Sky; Photometry; Metric; Graph theory; Statistical analysis; Vector support machine</ED>
<SD>Análisis discriminante; Alto rendimiento; Clasificación supervisada; Fuente puntual; Cielo; Fotometría; Métrico; Teoría grafo; Análisis estadístico; Máquina ejemplo soporte</SD>
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