Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Abstract

This work proposes a functional data analysis (FDA) approach for morphometrics in classifying three shrew species (S. murinus, C. monticola and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 90 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear models) using predicted PC scores obtained from both methods (combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species. Overall, the generalised linear models (GLM) was the most accurate (95.4% accuracy) among the four classification models.