Recruiting a skeleton crew-Methods for simulating and augmenting paleoanthropological data using Monte Carlo based algorithms.
3D model simulation
Markov chain Monte Carlo
data augmentation
geometric morphometrics
machine teaching
Journal
American journal of biological anthropology
ISSN: 2692-7691
Titre abrégé: Am J Biol Anthropol
Pays: United States
ID NLM: 101770171
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
revised:
31
01
2023
received:
22
09
2022
accepted:
26
04
2023
medline:
21
6
2023
pubmed:
18
5
2023
entrez:
18
5
2023
Statut:
ppublish
Résumé
Data collection is a major hindrance in many types of analyses in human evolutionary studies. This issue is fundamental when considering the scarcity and quality of fossil data. From this perspective, many research projects are impeded by the amount of data available to perform tasks such as classification and predictive modeling. Here we present the use of Monte Carlo based methods for the simulation of paleoanthropological data. Using two datasets containing cross-sectional biomechanical information and geometric morphometric 3D landmarks, we show how synthetic, yet realistic, data can be simulated to enhance each dataset, and provide new information with which to perform complex tasks with, in particular classification. We additionally present these algorithms in the form of an R library; AugmentationMC. We also use a geometric morphometric dataset to simulate 3D models, and emphasize the power of Machine Teaching, as opposed to Machine Learning. Our results show how Monte Carlo based algorithms, such as the Markov Chain Monte Carlo, are useful for the simulation of morphometric data, providing synthetic yet highly realistic data that has been tested statistically to be equivalent to the original data. We additionally provide a critical overview of bootstrapping techniques, showing how Monte Carlo based methods perform better than bootstrapping as the data simulated is not an exact copy of the original sample. While synthetic datasets should never replace large and real datasets, this can be considered an important advance in how paleoanthropological data can be handled.
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
454-473Informations de copyright
© 2023 Wiley Periodicals LLC.
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