Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44333
DC FieldValueLanguage
dc.contributor.authorNavega, David-
dc.contributor.authorCoelho, Catarina-
dc.contributor.authorVicente, Ricardo-
dc.contributor.authorFerreira, Maria Teresa-
dc.contributor.authorWasterlain, Sofia-
dc.contributor.authorCunha, Eugénia-
dc.date.accessioned2017-11-09T17:05:38Z-
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/10316/44333-
dc.description.abstractIn forensic anthropology, ancestry estimation is essential in establishing the individual biological profile. The aim of this study is to present a new program--AncesTrees--developed for assessing ancestry based on metric analysis. AncesTrees relies on a machine learning ensemble algorithm, random forest, to classify the human skull. In the ensemble learning paradigm, several models are generated and co-jointly used to arrive at the final decision. The random forest algorithm creates ensembles of decision trees classifiers, a non-linear and non-parametric classification technique. The database used in AncesTrees is composed by 23 craniometric variables from 1,734 individuals, representative of six major ancestral groups and selected from the Howells' craniometric series. The program was tested in 128 adult crania from the following collections: the African slaves' skeletal collection of Valle da Gafaria; the Medical School Skull Collection and the Identified Skeletal Collection of 21st Century, both curated at the University of Coimbra. The first step of the test analysis was to perform ancestry estimation including all the ancestral groups of the database. The second stage of our test analysis was to conduct ancestry estimation including only the European and the African ancestral groups. In the first test analysis, 75% of the individuals of African ancestry and 79.2% of the individuals of European ancestry were correctly identified. The model involving only African and European ancestral groups had a better performance: 93.8% of all individuals were correctly classified. The obtained results show that AncesTrees can be a valuable tool in forensic anthropology.por
dc.language.isoengpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147309/PTpor
dc.rightsembargoedAccess-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/por
dc.subjectAdultpor
dc.subjectAlgorithmspor
dc.subjectEthnic Groupspor
dc.subjectFemalepor
dc.subjectForensic Anthropologypor
dc.subjectHumanspor
dc.subjectMachine Learningpor
dc.subjectMalepor
dc.subjectSex Determination by Skeletonpor
dc.subjectCephalometrypor
dc.subjectContinental Population Groupspor
dc.subjectDatabases as Topicpor
dc.subjectDecision Treespor
dc.titleAncesTrees: ancestry estimation with randomized decision treespor
dc.typearticleeng
degois.publication.firstPage1145por
degois.publication.lastPage1153por
degois.publication.issue5por
degois.publication.titleInternational Journal of Legal Medicinepor
dc.relation.publisherversionhttp://link.springer.com/journal/414por
dc.peerreviewedyespor
dc.identifier.doi10.1007/s00414-014-1050-9por
degois.publication.volume129por
dc.date.embargo2018-11-09-
uc.controloAutoridadeSim-
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.languageiso639-1en-
crisitem.author.deptCFE - Centre for Functional Ecology - Science for People & the Planet-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCIAS - Research Centre for Anthropology and Health-
crisitem.author.researchunitCFE - Centre for Functional Ecology - Science for People & the Planet-
crisitem.author.orcid0000-0001-7338-4955-
crisitem.author.orcid0000-0003-2913-3037-
crisitem.author.orcid0000-0003-2998-371X-
Appears in Collections:I&D CIAS - Artigos em Revistas Internacionais
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