Automated Classification of Pointed Sources
Identifieur interne : 000B44 ( Main/Exploration ); précédent : 000B43; suivant : 000B45Automated Classification of Pointed Sources
Auteurs : YANXIA ZHANG [République populaire de Chine] ; YONGHENG ZHAO [République populaire de Chine] ; HONGWEN ZHENG [République populaire de Chine]Source :
- Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Astronomie.
English descriptors
- KwdEn :
Abstract
Facing very large and frequently high dimensional data in astronomy, effectiveness and efficiency of algorithms are always the hot issue. Excellent algorithms must avoid the curse of dimensionality and simultaneously should be computationally efficient. Adopting survey data from optical bands (SDSS, USNO-B1.0) and radio band (FIRST), we investigate feature weighting and feature selection by means of random forest algorithm. Then we employ a kd-tree based k-nearest neighbor method (KD-KNN) to discriminate quasars from stars. Then the performance of this approach based on all features, weighted features and selected features are compared. The experimental result shows that the accuracy improves when using weighted features or selected features. KD-KNN is a quite easy and efficient approach to nonparametric classification. Obviously KD-KNN combined with random forests is more effective to separate quasars from stars with multi-wavelength data.
Affiliations:
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Le document en format XML
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SPIE Int. Soc. Opt. Eng.</title><idno type="ISSN">0277-786X</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Proceedings of SPIE, the International Society for Optical Engineering</title><title level="j" type="abbreviated">Proc. SPIE Int. Soc. Opt. Eng.</title><idno type="ISSN">0277-786X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Astronomy</term><term>Automatic classification</term><term>Dimensionality</term><term>Graph theory</term><term>Multidimensional database</term><term>Nearest neighbour</term><term>Point source</term><term>Randomized algorithm</term><term>Wavelength</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Classification automatique</term><term>Plus proche voisin</term><term>Source ponctuelle</term><term>Base de données multidimensionnelle</term><term>Astronomie</term><term>Dimensionnalité</term><term>Longueur onde</term><term>Algorithme randomisé</term><term>Théorie graphe</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Astronomie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Facing very large and frequently high dimensional data in astronomy, effectiveness and efficiency of algorithms are always the hot issue. Excellent algorithms must avoid the curse of dimensionality and simultaneously should be computationally efficient. Adopting survey data from optical bands (SDSS, USNO-B1.0) and radio band (FIRST), we investigate feature weighting and feature selection by means of random forest algorithm. Then we employ a kd-tree based k-nearest neighbor method (KD-KNN) to discriminate quasars from stars. Then the performance of this approach based on all features, weighted features and selected features are compared. The experimental result shows that the accuracy improves when using weighted features or selected features. KD-KNN is a quite easy and efficient approach to nonparametric classification. 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