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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
26/12/2018 |
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Data da última atualização: |
24/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARVALHO, V. P.; SANT'ANNA, I. C.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; CRUZ, C. D.; ARBEX, W. A.; OLIVEIRA, F. C.; SILVA, F. F. |
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Afiliação: |
V. P. CARVALHO; I. C. SANT'ANNA; M. NASCIMENTO; A. C. C. NASCIMENTO; C. D. CRUZ; WAGNER ANTONIO ARBEX, CNPGL; F. C. OLIVEIRA; F. F. SILVA. |
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Título: |
Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 17, n. 4, gmr18122, 2018. |
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Páginas: |
10 p. |
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DOI: |
10.4238/gmr18122 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT Selection of appropriate genitors in breeding programs increases gains due to the variability found in the divergent groups; this allows quantification of the existing variability, saving time and resources. There are many methods for quantification and evaluation of diversity in population studies, among which we highlight methods that are based on multivariate statistical analyses, such as linear discriminant analysis (LDA) and cluster analysis. Here we propose and evaluate the use of Support Vector machine (SVM) and Artificial Neural Network (ANN) in an attempt to solve the problem of genetic classification of hybrid populations with high degrees of similarity. The results obtained, in terms of the apparent error rate (APER), were compared with those obtained using ANN analysis and LDA. In general, the lowest APER values were associated with scenarios with low degrees of genetic similarity between populations. Specifically, the best results obtained through SVM (ranging from 14.44 to 67.41%) were observed when the exponential radial base kernel function was used. The APERs obtained by the ANN were even lower than those of the linear discriminant function. |
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Palavras-Chave: |
Computational Intelligence; Multivariate approach. |
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Thesaurus Nal: |
Breeding. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02004naa a2200265 a 4500
001 2102505
005 2023-01-24
008 2018 bl uuuu u00u1 u #d
024 7 $a10.4238/gmr18122$2DOI
100 1 $aCARVALHO, V. P.
245 $aSupport vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity.$h[electronic resource]
260 $c2018
300 $a10 p.
520 $aABSTRACT Selection of appropriate genitors in breeding programs increases gains due to the variability found in the divergent groups; this allows quantification of the existing variability, saving time and resources. There are many methods for quantification and evaluation of diversity in population studies, among which we highlight methods that are based on multivariate statistical analyses, such as linear discriminant analysis (LDA) and cluster analysis. Here we propose and evaluate the use of Support Vector machine (SVM) and Artificial Neural Network (ANN) in an attempt to solve the problem of genetic classification of hybrid populations with high degrees of similarity. The results obtained, in terms of the apparent error rate (APER), were compared with those obtained using ANN analysis and LDA. In general, the lowest APER values were associated with scenarios with low degrees of genetic similarity between populations. Specifically, the best results obtained through SVM (ranging from 14.44 to 67.41%) were observed when the exponential radial base kernel function was used. The APERs obtained by the ANN were even lower than those of the linear discriminant function.
650 $aBreeding
653 $aComputational Intelligence
653 $aMultivariate approach
700 1 $aSANT'ANNA, I. C.
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aCRUZ, C. D.
700 1 $aARBEX, W. A.
700 1 $aOLIVEIRA, F. C.
700 1 $aSILVA, F. F.
773 $tGenetics and Molecular Research$gv. 17, n. 4, gmr18122, 2018.
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