Genetic classification of various familial relationships using the stacking ensemble machine learning approaches

초록

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is di_cult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying metaclassifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques. © (2024) The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.

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