Multi-classifier majority voting analyses in provenance studies on iron artefacts

Publication date: January 2020

Source: Journal of Archaeological Science, Volume 113

Author(s): Grzegorz Żabiński, Jarosław Gramacki, Artur Gramacki, Ewelina Miśta-Jakubowska, Thomas Birch, Alexandre Disser


The main objective of this paper is to propose an approach for identification of provenance of archaeological iron artefacts making use of major oxides and trace elements. For this purpose, seven classifiers were built on the basis of the following techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Random Forests (RF), Naïve Bayes (NB), K-Nearest Neighbours (KNN), Recursive Partitioning and Regression Trees (RPART) and Kernel Discriminant Analysis (KDA). A final assignment of a given observation to a regional class was carried out on the basis of results provided by all classifiers using a majority voting technique. The proposed approach was first tested on experimental slag and then it was applied to actual archaeological data. It is hoped that this method can become part of a new integrated approach which will consider all available types of data, such as major and trace elements and isotopic ratios.