2007/07/27 by Y. Zhang, Yanxia Zhang, Y. Zhao +3 · 4 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced Computational Techniques and Applications #Artificial intelligence #Astronomical Objects #Astronomical Observations and Instrumentation #Astrophysics #Computer science #Computer vision #Data mining #Decision table #Epoch (astronomy) #Feature (linguistics) #Mathematics #Pattern recognition (psychology) #Physics #Point (geometry) #Quasar #Rough set #Scale (ratio) #Stars #Table (database) #Time Series Analysis and Forecasting #astro-ph
paper · pdf · doi:10.1016/j.asr.2007.07.019
published in Advances in Space Research 41(12), 1949-1954 (Elsevier BV) · 10 pages. accepted by Advances in Space Research
openalex publication_date 2007/07/27 · arxiv created 2007/08/31 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
With the availability of multiwavelength, multiscale and multiepoch astronomical catalogues, the number of features to describe astronomical objects has increases. The better features we select to classify objects, the higher the classification accuracy is. In this paper, we have used data sets of stars and quasars from near infrared band and radio band. Then best-first search method was applied to select features. For the data with selected features, the algorithm of decision table was implemented. The classification accuracy is more than 95.9%. As a result, the feature selection method improves the effectiveness and efficiency of the classification method. Moreover the result shows that decision table is robust and effective for discrimination of celestial objects and used for preselecting quasar candidates for large survey projects.