Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/25154
DC Field | Value | Language |
---|---|---|
dc.contributor.author | D'Isanto, A. | en_US |
dc.contributor.author | Cavuoti, S. | en_US |
dc.contributor.author | BRESCIA, Massimo | en_US |
dc.contributor.author | Donalek, C. | en_US |
dc.contributor.author | Longo, G. | en_US |
dc.contributor.author | RICCIO, GIUSEPPE | en_US |
dc.contributor.author | Djorgovski, S. G. | en_US |
dc.date.accessioned | 2020-05-25T14:49:59Z | - |
dc.date.available | 2020-05-25T14:49:59Z | - |
dc.date.issued | 2016 | en_US |
dc.identifier.issn | 0035-8711 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12386/25154 | - |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.title | An analysis of feature relevance in the classification of astronomical transients with machine learning methods | en_US |
dc.type | Article | - |
dc.identifier.doi | 10.1093/mnras/stw157 | en_US |
dc.identifier.scopus | 2-s2.0-84976885060 | en_US |
dc.identifier.isi | 000373583900065 | en_US |
dc.identifier.url | https://academic.oup.com/mnras/article/457/3/3119/2588943 | en_US |
dc.relation.medium | STAMPA | en_US |
dc.relation.volume | 457 | en_US |
dc.relation.issue | 3 | en_US |
dc.relation.firstpage | 3119 | en_US |
dc.relation.lastpage | 3132 | en_US |
dc.relation.numberofpages | 13 | en_US |
dc.type.referee | REF_1 | en_US |
dc.description.numberofauthors | 7 | en_US |
dc.description.international | sì | en_US |
dc.contributor.country | ITA | en_US |
dc.contributor.country | USA | en_US |
dc.contributor.country | DEU | en_US |
dc.relation.scientificsector | FIS/05 - ASTRONOMIA E ASTROFISICA | en_US |
dc.relation.journal | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | en_US |
dc.type.miur | 262 Articolo in rivista | - |
dc.identifier.adsbibcode | 2016MNRAS.457.3119D | en_US |
dc.relation.ercsector | ERC sectors::Physical Sciences and Engineering::PE6 Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems::PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) | en_US |
dc.description.apc | no | en_US |
dc.description.oa | 1 – prodotto con file in versione Open Access (allegare il file al passo 5-Carica) | en_US |
local.message.claim | 2020-08-04T11:06:11.904+0200|||rp09320|||submit_approve|||dc_contributor_author|||None | * |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
crisitem.journal.journalissn | 0035-8711 | - |
crisitem.journal.ance | E112946 | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.orcid | 0000-0002-3787-4196 | - |
crisitem.author.orcid | 0000-0001-9506-5680 | - |
crisitem.author.orcid | 0000-0001-7020-1172 | - |
Appears in Collections: | 1.01 Articoli in rivista |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
DIsanto_etAl_2016_stw157.pdf | 2.14 MB | Adobe PDF | View/Open |
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