Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

Anna Anglisano, Lluís Casas (), Ignasi Queralt and Roberta Di Febo
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Anna Anglisano: Department of Geology, Campus de la UAB, Autonomous University of Barcelona, 08193 Barcelona, Spain
Lluís Casas: Department of Geology, Campus de la UAB, Autonomous University of Barcelona, 08193 Barcelona, Spain
Ignasi Queralt: Department of Geosciences, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Spain
Roberta Di Febo: Department of Geology, Campus de la UAB, Autonomous University of Barcelona, 08193 Barcelona, Spain

Sustainability, 2022, vol. 14, issue 18, 1-21

Abstract: 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.

Keywords: pottery; provenance studies; supervised methods; machine learning; clustering; XRF; data sharing; open source software; heritage science (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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