  This study explores the performance of modern, accurate machine learning
algorithms on the classification of fossil teeth in the Family Bovidae.
Isolated bovid teeth are typically the most common fossils found in southern
Africa and they often constitute the basis for paleoenvironmental
reconstructions. Taxonomic identification of fossil bovid teeth, however, is
often imprecise and subjective. Using modern teeth with known taxons, machine
learning algorithms can be trained to classify fossils. Previous work by Brophy
et. al. 2014 uses elliptical Fourier analysis of the form (size and shape) of
the outline of the occlusal surface of each tooth as features in a linear
discriminant analysis framework. This manuscript expands on that previous work
by exploring how different machine learning approaches classify the teeth and
testing which technique is best for classification. Five different machine
learning techniques including linear discriminant analysis, neural networks,
nuclear penalized multinomial regression, random forests, and support vector
machines were used to estimate these models. Support vector machines and random
forests perform the best in terms of both log-loss and misclassification rate;
both of these methods are improvements over linear discriminant analysis. With
the identification and application of these superior methods, bovid teeth can
be classified with higher accuracy.
