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Successful classification of experimental bone surface modifications (BSM) through machine learning algorithms: a solution to the controversial use of BSM in paleoanthropology?

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Abstract

Here, I show that powerful machine learning (ML) algorithms can efficiently classify most BSM of any given controlled experimental assemblage. In some cases, classification may reach an accuracy of 100%. No other statistical tool commonly used in taphonomy had been this successful at classification before. However, the heuristics of ML algorithms depend tightly on the objectivity in raw data collection. The use of multivariate approaches in which variables are independently scored by the analyst introduces a subjective bias. In this work, I show that different analysts producing raw data on the same testing data set can lead to widely divergent BSM classifications and interpretations using the same powerful ML algorithms. It is emphasized that until an objective non-biasing method of raw data collection is implemented, BSM classification carried out via statistical tests will remain heuristically limited. As a consequence, the application of these referents to archeological BSM does not guarantee a correct interpretation beyond the analyst expertise.

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Acknowledgments

MDR thanks D. Lieberman and the Human Evolutionary Biology Department at Harvard and the Royal Complutense College at Harvard, where this research was conducted. I thank Ruth Blasco, Nick Conard, and two anonymous reviewers for their constructive comments on an earlier draft of this manuscript. I also thank my colleagues P. Saladié, I. Cáceres, R. Huguet, J. Yravedra, A. Rodríguez-Hidalgo, P. Martín, A. Pineda, J. Marín, C. Gené, J. Aramendi, and L. Cobo-Sánchez for their joint work in the experimental analysis that led to inter-analyst comparisons of BSM.

Funding

This work was carried out with support from a Research Salvador Madariaga grant to MDR (Ministry of Education, Culture and Sport, Spain, Ref. PRX16/00010).

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Authors and Affiliations

  1. Institute of Evolution in Africa (IDEA), University of Alcalá de Henares, Covarrubias 36, 28010, Madrid, Spain

    Manuel Domínguez-Rodrigo

  2. Department of Prehistory, Complutense University, 28040, Madrid, Spain

    Manuel Domínguez-Rodrigo

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Correspondence to Manuel Domínguez-Rodrigo.

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Domínguez-Rodrigo, M. Successful classification of experimental bone surface modifications (BSM) through machine learning algorithms: a solution to the controversial use of BSM in paleoanthropology?. Archaeol Anthropol Sci 11, 2711–2725 (2019). https://doi.org/10.1007/s12520-018-0684-9

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