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  1. OA@INAF
  2. PRODOTTI RICERCA INAF
  3. 1 CONTRIBUTI IN RIVISTE (Journal articles)
  4. 1.01 Articoli in rivista
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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