YanXia Zhang; YongHeng Zhao
Source Publicationchinesejournalofastronomyandastrophysics
AbstractWe compare the performance of Bayesian Belief Networks (BBN), Multilayer Perception (MLP) networks and Alternating Decision Trees (ADtree) on separating quasars from stars with the database from the 2MASS and FIRST survey catalogs. Having a training sample of sources of known object types, the classifiers are trained to separate quasars from stars. By the statistical properties of the sample, the features important for classification are selected. We compare the classification results with and without feature selection. Experiments show that the results with feature selection are better than those without feature selection. From the high accuracy found, it is concluded that these automated methods are robust and effective for classifying point sources. They may all be applied to large survey projects (e.g. selecting input catalogs) and for other astronomical issues, such as the parameter measurement of stars and the redshift estimation of galaxies and quasars.
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
YanXia Zhang,YongHeng Zhao. acomparisonofbbnadtreeandmlpinseparatingquasarsfromlargesurveycatalogues[J]. chinesejournalofastronomyandastrophysics,2007,7(2):289.
APA YanXia Zhang,&YongHeng Zhao.(2007).acomparisonofbbnadtreeandmlpinseparatingquasarsfromlargesurveycatalogues.chinesejournalofastronomyandastrophysics,7(2),289.
MLA YanXia Zhang,et al."acomparisonofbbnadtreeandmlpinseparatingquasarsfromlargesurveycatalogues".chinesejournalofastronomyandastrophysics 7.2(2007):289.
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