A unified approach for morphometrics and functional data analysis with machine learning for craniodental shape quantification in shrew species

TL;DR

This paper introduces a functional data analysis method combined with machine learning to improve the classification of shrew species based on craniodental shape data, outperforming classical approaches.

Contribution

It presents a novel FDA-based framework for morphometrics that enhances species classification accuracy using continuous curve representations and machine learning.

Findings

FDA approach outperforms classical methods in species separation

Dorsal view provides the best classification representation

GLM with PCA scores achieves 95.4% accuracy

Abstract

This work proposes a functional data analysis approach for morphometrics with applications in classifying three shrew species (S. murinus, C. monticola and C. malayana) based on the images. The discrete landmark data of craniodental views (dorsal, jaw and lateral) are converted into continuous curves where the curves are represented as linear combinations of basis functions. A comparative study based on four machine learning algorithms such as naive Bayes, support vector machine, random forest, and generalized linear models was conducted on the predicted principal component scores obtained from the FDA approach and classical approach (combination of all three craniodental views and individual views). The FDA approach produced better results in separating the three clusters of shrew species compared to the classical method and the dorsal view gave the best representation in classifying the three shrew species. Overall, based on the FDA approach, GLM of the predicted PCA scores was the most accurate (95.4% accuracy) among the four classification models.