Genetic classification of various familial relationships using the stacking ensemble machine learning approaches Article Swipe
Su Jin Jeong
,
Hyo‐Jung Lee
,
Soong Deok Lee
,
Ji Eun Park
,
Jae Won Lee
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.29220/csam.2024.31.3.279
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.29220/csam.2024.31.3.279
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 metaclassifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.29220/csam.2024.31.3.279
- http://www.csam.or.kr/journal/download_pdf.php?doi=10.29220/CSAM.2024.31.3.279
- OA Status
- diamond
- References
- 22
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4399247028Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.29220/csam.2024.31.3.279Digital Object Identifier
- Title
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Genetic classification of various familial relationships using the stacking ensemble machine learning approachesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-31Full publication date if available
- Authors
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Su Jin Jeong, Hyo‐Jung Lee, Soong Deok Lee, Ji Eun Park, Jae Won LeeList of authors in order
- Landing page
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https://doi.org/10.29220/csam.2024.31.3.279Publisher landing page
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https://www.csam.or.kr/journal/download_pdf.php?doi=10.29220/CSAM.2024.31.3.279Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.csam.or.kr/journal/download_pdf.php?doi=10.29220/CSAM.2024.31.3.279Direct OA link when available
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Stacking, Ensemble learning, Artificial intelligence, Machine learning, Computer science, Genetic algorithm, Pattern recognition (psychology), Chemistry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 102 |
| abstract_inverted_index.above | 63 |
| abstract_inverted_index.apply | 56, 75 |
| abstract_inverted_index.first | 70 |
| abstract_inverted_index.novel | 41 |
| abstract_inverted_index.ratio | 33 |
| abstract_inverted_index.these | 57 |
| abstract_inverted_index.third | 65 |
| abstract_inverted_index.total | 25 |
| abstract_inverted_index.used, | 39 |
| abstract_inverted_index.(TNSA) | 30 |
| abstract_inverted_index.(e.g., | 67 |
| abstract_inverted_index.degree | 66 |
| abstract_inverted_index.mining | 116 |
| abstract_inverted_index.number | 26 |
| abstract_inverted_index.obtain | 22, 94 |
| abstract_inverted_index.rather | 106 |
| abstract_inverted_index.shared | 28 |
| abstract_inverted_index.useful | 4 |
| abstract_inverted_index.alleles | 29 |
| abstract_inverted_index.applied | 48 |
| abstract_inverted_index.genetic | 10 |
| abstract_inverted_index.improve | 83 |
| abstract_inverted_index.machine | 79 |
| abstract_inverted_index.methods | 35, 44, 58, 113 |
| abstract_inverted_index.propose | 73 |
| abstract_inverted_index.results | 98 |
| abstract_inverted_index.Familial | 0 |
| abstract_inverted_index.accuracy | 85 |
| abstract_inverted_index.applying | 100, 108 |
| abstract_inverted_index.ensemble | 78 |
| abstract_inverted_index.familial | 19, 61, 87 |
| abstract_inverted_index.forensic | 8 |
| abstract_inverted_index.identify | 16, 60 |
| abstract_inverted_index.learning | 80 |
| abstract_inverted_index.possible | 14 |
| abstract_inverted_index.recently | 46 |
| abstract_inverted_index.stacking | 77, 104 |
| abstract_inverted_index.superior | 95 |
| abstract_inverted_index.algorithm | 81, 105 |
| abstract_inverted_index.analysis, | 92 |
| abstract_inverted_index.determine | 18 |
| abstract_inverted_index.difficult | 54 |
| abstract_inverted_index.searching | 1 |
| abstract_inverted_index.technique | 5 |
| abstract_inverted_index.likelihood | 32 |
| abstract_inverted_index.data-mining | 42 |
| abstract_inverted_index.techniques. | 117 |
| abstract_inverted_index.traditional | 109 |
| abstract_inverted_index.individuals, | 17 |
| abstract_inverted_index.information, | 11 |
| abstract_inverted_index.relationship | 88, 96 |
| abstract_inverted_index.uncle-nephew | 68 |
| abstract_inverted_index.racial/ethnic | 23 |
| abstract_inverted_index.relationships | 62 |
| abstract_inverted_index.traditionally | 37 |
| abstract_inverted_index.well.However, | 51 |
| abstract_inverted_index.classification | 43 |
| abstract_inverted_index.identification | 97 |
| abstract_inverted_index.relationships, | 20 |
| abstract_inverted_index.information.The | 24 |
| abstract_inverted_index.metaclassifiers | 101 |
| abstract_inverted_index.cousins).Therefore, | 71 |
| abstract_inverted_index.investigation.Using | 9 |
| abstract_inverted_index.identification.Using | 89 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.09562462 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |