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This work proposes a functional data analysis approach for morphometrics withapplications in classifying three shrew species (S. murinus, C. monticola andC. malayana) based on the images. The discrete landmark data of craniodentalviews (dorsal, jaw and lateral) are converted into continuous curves where thecurves are represented as linear combinations of basis functions. A comparativestudy based on four machine learning algorithms such as naive Bayes, supportvector machine, random forest, and generalized linear models was conducted onthe predicted principal component scores obtained from the FDA approach andclassical approach (combination of all three craniodental views and individualviews). The FDA approach produced better results in separating the threeclusters of shrew species compared to the classical method and the dorsal viewgave the best representation in classifying the three shrew species. Overall,based on the FDA approach, GLM of the predicted PCA scores was the mostaccurate (95.4% accuracy) among the four classification models.

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