Multi-classifier majority voting analyses in provenance studies on iron artefacts
Grzegorz Żabiński|J. Gramacki|Artur Gramacki|Ewelina Miśta‐Jakubowska|Thomas Birch|Alexandre Disser
The main objective of this paper is to propose an approach for identification provenance archaeological iron artefacts making use major oxides and trace elements. For purpose, seven classifiers were built on the basis following techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Random Forests (RF), Naïve Bayes (NB), K-Nearest Neighbours (KNN), Recursive Partitioning Regression Trees (RPART) Kernel (KDA). A final assignment a given observation regional class was carried out results provided by all using majority voting technique. proposed first tested experimental slag then it applied actual data. It hoped that method can become part new integrated which will consider available types data, such as elements isotopic ratios.
Journal of Archaeological Science|HAL (Le Centre pour la Communication Scientifique Directe)|HAL (Le Centre pour la Communication Scientifique Directe)|Scopus (Elsevier)