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Ancestry Assessment Using Random Forest Modeling , ,
Author(s) -
Hefner Joseph T.,
Spradley M. Kate,
Anderson Bruce
Publication year - 2014
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12402
Subject(s) - discriminant function analysis , random forest , statistics , linear discriminant analysis , population , sample size determination , mathematics , pattern recognition (psychology) , artificial intelligence , computer science , medicine , environmental health
A skeletal assessment of ancestry relies on morphoscopic traits and skeletal measurements. Using a sample of A merican B lack ( n = 38), A merican W hite ( n = 39), and S outhwest H ispanics ( n = 72), the present study investigates whether these data provide similar biological information and combines both data types into a single classification using a random forest model ( RFM ). Our results indicate that both data types provide similar information concerning the relationships among population groups. Also, by combining both in an RFM , the correct allocation of ancestry for an unknown cranium increases. The distribution of cross‐validated grouped cases correctly classified using discriminant analyses and RFM s ranges between 75.4% (discriminant function analysis, morphoscopic data only) and 89.6% ( RFM ). Unlike the traditional, experience‐based approach using morphoscopic traits, the inclusion of both data types in a single analysis is a quantifiable approach accounting for more variation within and between groups, reducing misclassification rates, and capturing aspects of cranial shape, size, and morphology.