Communications for Statistical Applications and Methods

  • 제31권3호
  • /
  • Pages.279-289
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  • 2024
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Genetic classification of various familial relationships using the stacking ensemble machine learning approaches

  • Su Jin Jeong (Statistics Support Part, Medical Science Research Institute, Kyung Hee University Medical Center) ;
  • Hyo-Jung Lee (Product Development HQ, Dong-A ST) ;
  • Soong Deok Lee (Department of Forensic Medicine, College of Medicine, Seoul National University) ;
  • Ji Eun Park (Department of Statistics, Korea University) ;
  • Jae Won Lee (Department of Statistics, Korea University)
  • 투고 : 2023.05.24
  • 심사 : 2024.01.12
  • 발행 : 2024.05.31
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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 difficult 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 meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

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과제정보

This research was supported and funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00208882) and the Korean National Police Agency [Project Name: Advancing the Appraisal Techniques of Forensic Entomology / Project Number: PR10-04-000-22].

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