Titre :

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

Auteur(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]

Titre de la revue :

Scientific Reports

Pagination :

15579

Éditeur :

Nature Publishing Group

Date de publication :

2024-07-06

ISSN :

2045-2322

Discipline(s) HAL :

Statistiques [stat]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL