Mapping Ancestry through Surnames: Machine Learning Approaches Applied to Brazilian Data - Lins Wolmer, Arthur; de Freitas Bezerra, Diego (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2025)
The classification of surname origin as a proxy for ethnic background estimation has long supported sociological, demographic, and genetic studies, particularly in countries with diverse migratory histories. In this article, we introduce a new Brazilian dataset constructed from over one million historical immigration records, propose a pipeline for surname extraction and disambiguation, and evaluate multiple supervised classifiers based on character-level n-grams. In addition to replicating classical models, we implement graph-based methods and an ensemble classifier. Our results confirm the competitiveness of traditional approaches while achieving significant gains with the ensemble model.