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

Elliot, Tom ORCID: 0000-0002-5249-2671, Morse, Robert, Smythe, Duane and Norris, Ashley (2021) Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain. SCIENTIFIC REPORTS, 11 (1). 10197-.

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Official URL: http://dx.doi.org/10.1038/s41598-021-87834-3

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.

Item Type:	Article
Uncontrolled Keywords:	15 Life on Land
Divisions:	Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures
Depositing User:	Symplectic Admin
Date Deposited:	15 Jun 2021 15:40
Last Modified:	17 Mar 2024 11:57
DOI:	10.1038/s41598-021-87834-3
Open Access URL:	https://doi.org/10.1038/s41598-021-87834-3
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3126465