Abstract
This study presents a method for identifying small subsets of morphological attributes of the skeletal pelvis that have consistently high reliability in assigning the sex of unknown individuals. An inductive computer algorithm (ID3) was applied to a bootstrapped training set/test set design in which the model was developed from 70% of the sample and tested on the remaining 30%. Relative accuracy of sex classification was evaluated for seven subsets of 31 morphological features of the adult os coxae. Using 115 ossa coxarum selected from the Terry Collection, a selected suite of the three most consistently diagnostic attributes averaged 93.1% correct classification of individuals by sex over ten trials. Attribute suites developed collaboratively with three well known skeletal experts averaged 87.8, 91.3, and 89.6% correct. The full set of 31 attributes averaged 90.0% accuracy. We demonstrate a small set of three criteria, selected and ordered by ID3, that is more accurate than other combinations, and suggest that ID3 is a useful approach for developing identification systems.
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