There is Strength in Numbers: A Comprehensive Study of Machine Learning Algorithms for Sex Identification on Animal Bone Remains

• Published in: 2024 IEEE 20th International Conference on e-Science (e-Science) pp. 1 - 10
• Main Authors: Schuler, Nadine-Sarah, Paxinos, Ptolemaios Dimitrios, Yuan, Jing, von Zastrow, Maximilian, Peters, Joris, Kroger, Peer
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.09.2024
• Subjects: Accuracy; algorithms; Animals; archaeological data; Bones; History; Machine learning; Machine learning algorithms; Neural networks; sex dimorphism; sheep bones; talus
• DOI: 10.1109/e-Science62913.2024.10678723

This study explores the application of supervised and unsupervised machine learning algorithms for predicting the sex of sheep using measurements of the talus bone in archaeozoological research. Leveraging data from well-documented sheep populations, we trained and tested various machine learning algorithms, such as kNN, SVMs, Decision Trees, Neural Networks, k-Means, DBSCAN, and GMM - demonstrating high accuracy in sex classification across multiple datasets from various time periods. We furthermore evaluate a variety of clustering results on unlabeled data and highlight their respective strengths and drawbacks. Our results suggest that machine learning offers a promising direction for enhancing the analysis of ancient and recent animal remains, providing valuable insights into past animal husbandry practices and their implications for understanding human history.