Title :

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

Author(s) :

Pillay, Aneesha Balachandran [Auteur]
Pathmanathan, Dharini [Auteur]
Dabo-Niang, Sophie [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Abu, Arpah [Auteur]
Omar, Hasmahzaiti [Auteur]

Journal title :

Scientific Reports

Pages :

15579

Publisher :

Nature Publishing Group

Publication date :

2024-07-06

ISSN :

2045-2322

HAL domain(s) :

Statistiques [stat]

English abstract : [en]

Abstract This work proposes a functional data analysis approach for morphometrics in classifying three shrew species ( S . murinus , C . monticola , and C . malayana ) from Peninsular Malaysia. Functional data geometric ...
Show more >Abstract This work proposes a functional data analysis 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 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis 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 model) using predicted PC scores obtained from both methods (a 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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL