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

Author:

Pillay Aneesha Balachandran1,Pathmanathan Dharini1,Dabo-Niang Sophie2,Abu Arpah2,Omar Hasmahzaiti1

Affiliation:

1. Universiti Malaya

2. INRIA-MODAL, UniversitédeUniversitéde Lelle

Abstract

Abstract This work proposes a functional data analysis (FDA) 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 90 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis (PCA) and linear discriminant analysis (LDA) 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 models) using predicted PC scores obtained from both methods (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. Overall, the generalised linear models (GLM) was the most accurate (95.4% accuracy) among the four classification models.

Publisher

Research Square Platform LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3