2023/02/20 by Aneesha Balachandran Pillay, Dharini Pathmanathan, Pillay, Aneesha Balachandran +7
Agricultural and Biological Sciences · Environmental Science · Mathematics · #Bat Biology and Ecology Studies #FOS: Biological sciences #I.5.3 #Morphological variations and asymmetry #Quantitative Methods (q-bio.QM) #Species Distribution and Climate Change
paper · pdf · doi:10.48550/arxiv.2302.10792
openalex publication_date 2023/02/20 · openalex created_date 2023/02/24 · openalex updated_date 2026/07/28
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.