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		<doi>10.1093/mnras/staa1463</doi>
		<issn>0035-8711</issn>
		<issn>1365-2966</issn>
		<citationkey>CarrubaAljDomLucFur:2020:MaLeCl</citationkey>
		<title>Machine learning classification of new asteroid families members</title>
		<year>2020</year>
		<month>Jun</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Carruba, Valerio,</author>
		<author>Aljbaae, Safwan,</author>
		<author>Domingos, R. C.,</author>
		<author>Lucchini, A.,</author>
		<author>Furlaneto, P.,</author>
		<orcid>0000-0003-2786-0740</orcid>
		<group></group>
		<group>DIDMC-CGETE-INPE-MCTIC-GOV-BR</group>
		<affiliation>Universidade Estadual Paulista (UNESP)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade Estadual Paulista (UNESP)</affiliation>
		<affiliation>Universidade Estadual Paulista (UNESP)</affiliation>
		<affiliation>Universidade Estadual Paulista (UNESP)</affiliation>
		<electronicmailaddress>valerio.carruba@unesp.br</electronicmailaddress>
		<electronicmailaddress>safwan.aljbaae@inpe.br</electronicmailaddress>
		<journal>Monthly Notices of the Royal Astronomical Society</journal>
		<volume>496</volume>
		<number>1</number>
		<pages>540-549</pages>
		<secondarymark>A1_QUÍMICA A1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_III A2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA A2_ASTRONOMIA_/_FÍSICA B2_ENSINO B5_ENGENHARIAS_IV</secondarymark>
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		<contenttype>External Contribution</contenttype>
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		<keywords>software: data analysis – celestial mechanics – minor planets, asteroids: general.</keywords>
		<abstract>Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object. These groups are mainly identified in proper elements or frequencies domains. Because of robotic telescope surveys, the number of known asteroids has increased from 10 000 in the early 1990s to more than 750 000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a, e, sin (i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand-alone and ensemble approaches. The extremely randomized trees (ExtraTree) method had the highest precision, enabling to retrieve up to 97 per cent of family members identified with standard HCM.</abstract>
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		<language>en</language>
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