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Statistical Classification Methods for Estimating Ancestry Using Morphoscopic Traits ,
Author(s) -
Hefner Joseph T.,
Ousley Stephen D.
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 - 10.1111/1556-4029.12421
Subject(s) - trait , random forest , support vector machine , artificial intelligence , observer (physics) , statistics , artificial neural network , machine learning , computer science , pattern recognition (psychology) , mathematics , physics , quantum mechanics , programming language
Ancestry assessments using cranial morphoscopic traits currently rely on subjective trait lists and observer experience rather than empirical support. The trait list approach, which is untested, unverified, and in many respects unrefined, is relied upon because of tradition and subjective experience. Our objective was to examine the utility of frequently cited morphoscopic traits and to explore eleven appropriate and novel methods for classifying an unknown cranium into one of several reference groups. Based on these results, artificial neural networks ( aNN s), OSSA , support vector machines, and random forest models showed mean classification accuracies of at least 85%. The aNN s had the highest overall classification rate (87.8%), and random forests show the smallest difference between the highest (90.4%) and lowest (76.5%) classification accuracies. The results of this research demonstrate that morphoscopic traits can be successfully used to assess ancestry without relying only on the experience of the observer.