Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/25154
Title: | An analysis of feature relevance in the classification of astronomical transients with machine learning methods | Authors: | D'Isanto, A. Cavuoti, S. BRESCIA, Massimo Donalek, C. Longo, G. RICCIO, GIUSEPPE Djorgovski, S. G. |
Issue Date: | 2016 | Journal: | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | Number: | 457 | Issue: | 3 | First Page: | 3119 | Abstract: | The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae. | URI: | http://hdl.handle.net/20.500.12386/25154 | URL: | https://academic.oup.com/mnras/article/457/3/3119/2588943 | ISSN: | 0035-8711 | DOI: | 10.1093/mnras/stw157 | Bibcode ADS: | 2016MNRAS.457.3119D | Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista |
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DIsanto_etAl_2016_stw157.pdf | 2.14 MB | Adobe PDF | View/Open |
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