Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Abstract It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in...

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Autores principales:	Tom Elliot, Robert Morse, Duane Smythe, Ashley Norris
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/05d042952e384d4ca5008c1d30e22c6e
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spelling	oai:doaj.org-article:05d042952e384d4ca5008c1d30e22c6e2021-12-02T17:15:22ZEvaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain10.1038/s41598-021-87834-32045-2322https://doaj.org/article/05d042952e384d4ca5008c1d30e22c6e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87834-3https://doaj.org/toc/2045-2322Abstract It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.Tom ElliotRobert MorseDuane SmytheAshley NorrisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tom Elliot Robert Morse Duane Smythe Ashley Norris Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
description	Abstract It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.
format	article
author	Tom Elliot Robert Morse Duane Smythe Ashley Norris
author_facet	Tom Elliot Robert Morse Duane Smythe Ashley Norris
author_sort	Tom Elliot
title	Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
title_short	Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
title_full	Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
title_fullStr	Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
title_full_unstemmed	Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
title_sort	evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in britain
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/05d042952e384d4ca5008c1d30e22c6e
work_keys_str_mv	AT tomelliot evaluatingmachinelearningtechniquesforarchaeologicallithicsourcingacasestudyofflintinbritain AT robertmorse evaluatingmachinelearningtechniquesforarchaeologicallithicsourcingacasestudyofflintinbritain AT duanesmythe evaluatingmachinelearningtechniquesforarchaeologicallithicsourcingacasestudyofflintinbritain AT ashleynorris evaluatingmachinelearningtechniquesforarchaeologicallithicsourcingacasestudyofflintinbritain
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Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

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