Supervised learning algorithms in the classification of plant populations with different degrees of kinship

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Catolica Dom Bosco-
Autor(es): dc.contributorFed Univ Grande Dourados-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorFed Inst Educ Sci & Technol Mato Grosso do Sul-
Autor(es): dc.creatorSkowronski, Leandro-
Autor(es): dc.creatorMoraes, Paula Martin de-
Autor(es): dc.creatorTeixeira de Moraes, Mario Luiz [UNESP]-
Autor(es): dc.creatorGoncalves, Wesley Nunes-
Autor(es): dc.creatorConstantino, Michel-
Autor(es): dc.creatorCosta, Celso Soares-
Autor(es): dc.creatorFava, Wellington Santos-
Autor(es): dc.creatorCosta, Reginaldo B.-
Data de aceite: dc.date.accessioned2022-02-22T00:55:35Z-
Data de disponibilização: dc.date.available2022-02-22T00:55:35Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-02-03-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s40415-021-00703-1-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209174-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209174-
Descrição: dc.descriptionThe population discrimination and the classification of individuals have great importance for genetic improvement in population studies and genetic diversity conservation. Furthermore, multivariate approaches are often used, especially the Fisher and Anderson discriminant functions. New methodologies based on machine learning (ML) have shown to be promising for such procedures, but there is nonetheless a need for further evaluation and comparison of these methods. Thus, the present study evaluates the efficacy of supervised ML algorithms in classifying populations with different degrees of similarity-comparing them with discriminant analysis techniques proposed by Anderson and by Fisher. The methods of supervised ML tested were as follows: Naive Bayes, Decision Tree, k-Nearest Neighbors (kNN), Random Forest, Support Vector Machine (SVM) and Multi-layer Perceptron Neural Networks (MLP/ANN). To compare classification methods, we used phenotypic data of populations with different degrees of genetic similarity. Data stemmed from the genotypic information simulation for different populations submitted to the backcrossing scheme. Accuracy here means 30 repetitions from each classification method were compared by the Friedman and Nemenyi tests with a 95% confidence level. Classification methods based on machine learning algorithms showed superior results to the Fisher and Anderson discriminant functions, obtaining high accuracy where there was a higher similarity between populations. The kNN, Random Forest, SVM and Naive Bayes algorithms presented the highest accuracy, surpassing the Decision Tree algorithm and even MLP/ANN (which lost accuracy at a 96.88% similarity condition between populations). Thus, the present work confirms that ML techniques demonstrate greater accuracy in the discrimination and classification of populations without the limitations of statistical techniques.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionUniv Catolica Dom Bosco, Campo Grande, MS, Brazil-
Descrição: dc.descriptionFed Univ Grande Dourados, Dourados, MS, Brazil-
Descrição: dc.descriptionPaulista State Univ Julio de Mesquita Filho, Ilha Solteira, SP, Brazil-
Descrição: dc.descriptionUniv Fed Mato Grosso do Sul, Campo Grande, MS, Brazil-
Descrição: dc.descriptionFed Inst Educ Sci & Technol Mato Grosso do Sul, Campo Grande, MS, Brazil-
Descrição: dc.descriptionUniv Fed Mato Grosso do Sul, Inst Biosci, Lab Ecol & Evolutionary Biol, BR-79070900 Campo Grande, MS, Brazil-
Descrição: dc.descriptionPaulista State Univ Julio de Mesquita Filho, Ilha Solteira, SP, Brazil-
Descrição: dc.descriptionCAPES: PNPD/CAPES 88882.315120/2019-01-
Descrição: dc.descriptionCAPES: CNPq 301840/2016-4-
Formato: dc.format9-
Idioma: dc.languageen-
Publicador: dc.publisherSoc Botanica Sao Paulo-
Relação: dc.relationBrazilian Journal Of Botany-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectClassification methods-
Palavras-chave: dc.subjectGenetic improvement-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectSimilarity between populations-
Título: dc.titleSupervised learning algorithms in the classification of plant populations with different degrees of kinship-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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