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.

An analysis of feature relevance in the classification of astronomical transients with machine learning methods / D'Isanto, A.; Cavuoti, S.; Brescia, M.; Donalek, C.; Longo, G.; Riccio, G.; Djorgovski, S. G.. - In: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY. - ISSN 0035-8711. - 457:3(2016), pp. 3119-3132. [10.1093/mnras/stw157]

An analysis of feature relevance in the classification of astronomical transients with machine learning methods

Brescia, M.;Longo, G.;
2016

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.
2016
An analysis of feature relevance in the classification of astronomical transients with machine learning methods / D'Isanto, A.; Cavuoti, S.; Brescia, M.; Donalek, C.; Longo, G.; Riccio, G.; Djorgovski, S. G.. - In: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY. - ISSN 0035-8711. - 457:3(2016), pp. 3119-3132. [10.1093/mnras/stw157]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/677679
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