Details

Autor(en) / Beteiligte

• Hefner, Joseph T.
• Ousley, Stephen D.

Titel

Statistical Classification Methods for Estimating Ancestry Using Morphoscopic Traits

Ist Teil von

• Journal of forensic sciences, 2014-07, Vol.59 (4), p.883-890

Ort / Verlag

United States: Blackwell Publishing Ltd

Erscheinungsjahr

2014

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 (aNNs), OSSA, support vector machines, and random forest models showed mean classification accuracies of at least 85%. The aNNs 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.