Supervised learning algorithms as a tool for archaeology: Classification of ceramic samples described by chemical element concentrations

Ceramic provenance studies often use minor and trace elements to gather knowledge about the presence of local furnaces and commercial trades. There are various chemical techniques that can be used to determine the elemental composition of ceramics, either non-destructively or by requiring samples. From these data, researchers can often determine provenance, and then use multivariate analyses with geological and archaeological information to classify the ceramics. In this study, we aimed to demonstrate the potential of supervised Machine Learning techniques to classify ceramic samples based on their chemical element concentrations. We applied several supervised learning algorithms to a set of 36 fragments whose archaeological classification was already known, using chemical analysis data that had been verified through previous studies. We carried out different sets of experiments, exploiting in different ways the available data, and evaluated the performance of the adopted algorithms, to propose new tools for ceramics provenance studies in archaeology. Our results show that machine learning can be a reliable and useful tool for archaeological classification based on chemical analysis data, providing a reliable and schematic picture of archaeological findings.