Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments
Anglisano, Anna (Universitat Autònoma de Barcelona. Departament de Geologia)
Casas Duocastella, Lluís (Universitat Autònoma de Barcelona. Departament de Geologia)
Queralt, Ignasi (Institut de Diagnosi Ambiental i Estudis de l'Aigua)
Di Febo, Roberta (Universitat Autònoma de Barcelona. Departament de Geologia)

Data:	2022
Resum:	Code and data sharing are crucial practices to advance toward sustainable archaeology. This article explores the performance of supervised machine learning classification methods for provenancing archaeological pottery through the use of freeware R code in the form of R Markdown files. An illustrative example was used to show all the steps of the new methodology, starting from the requirements to its implementation, the verification of its classification capability and finally, the production of cluster predictions. The example confirms that supervised methods are able to distinguish classes with similar features, and provenancing is achievable. The provided code contains self-explanatory notes to guide the users through the classification algorithms. Archaeometrists without previous knowledge of R should be able to apply the novel methodology to similar well-constrained classification problems. Experienced users could fully exploit the code to set up different combinations of parameters, and they could further develop it by adding other classification algorithms to suit the requirements of diverse classification strategies.
Ajuts:	Ministerio de Ciencia e Innovación CEX2018-000794-S
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Pottery ; Provenance studies ; Supervised methods ; Machine learning ; Clustering ; XRF ; Data sharing ; Open source software ; Heritage science
Publicat a:	Sustainability, Vol. 14, Issue 18 (September 2022) , art. 11214, ISSN 2071-1050

DOI: 10.3390/su141811214

21 p, 2.1 MB

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