An analysis of feature relevance in the classification of astronomical transients with machine learning methods
Date Issued
2016
Author(s)
D'Isanto, A.
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•
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Donalek, C.
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Longo, G.
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Djorgovski, S. G.
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.
Volume
457
Issue
3
Start page
3119
Issn Identifier
0035-8711
Ads BibCode
2016MNRAS.457.3119D
Rights
open.access
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