ÁRVORE DE DECISÃO COMO FERRAMENTA NA CLASSIFICAÇÃO DE ACESSOS DE FEIJÃO-FAVA

Rafael da Costa Almeida; Wilson Vitorino de Assunção Neto; Verônica Brito da Silva; Leonardo Castelo Branco Carvalho; Ângela Celis de Almeida Lopes; Regina Lucia Ferreira Gomes

doi:10.1590/1983-21252021v34n223rc

Authors

Rafael da Costa Almeida Universidade Federal do Piauí, Teresina, PI https://orcid.org/0000-0001-8067-546X
Wilson Vitorino de Assunção Neto Universidade Federal do Piauí, Teresina, PI https://orcid.org/0000-0002-0499-6058
Verônica Brito da Silva Department of Plant Science, Universidade Federal do Piauí, Teresina, PI https://orcid.org/0000-0001-7739-5061
Leonardo Castelo Branco Carvalho Department of Plant Science, Universidade Federal do Piauí, Teresina, PI https://orcid.org/0000-0001-5722-9322
Ângela Celis de Almeida Lopes Department of Plant Science, Universidade Federal do Piauí, Teresina, PI https://orcid.org/0000-0002-9546-5403
Regina Lucia Ferreira Gomes Department of Plant Science, Universidade Federal do Piauí, Teresina, PI https://orcid.org/0000-0002-7700-6959

DOI:

https://doi.org/10.1590/1983-21252021v34n223rc

Keywords:

Phaseolus lunatus L. Machine learning. Computational intelligence. Multivariate methods.

Abstract

Morpho-agronomic characterization studies aiming at the discrimination and classification of lima bean accessions in relation to the centers of domestication and biological status have been of great importance for conserving the biodiversity of this species. For this purpose, researchers have widely used the multivariate analysis called discriminant analysis, which is not always capable of producing satisfactory results. Computational intelligence-based classifiers are additional tools for understanding complex classification problems. In this study, the objective was to test the use of the decision tree in the classification of lima bean according to the centers of domestication and biological status (cultivated and wild), based on eight phenotypic traits of the seed. Sixty accessions of lima bean from the Phaseolus Germplasm Bank of Universidade Federal do Piauí (BGP / UFPI) were evaluated, and classification was performed using two approaches: conventional statistics with discriminant analysis of principal components (DAPC) and computational intelligence through decision tree (DT). The results showed that the use of DT was efficient to identify patterns in the classification of lima bean accessions, due to its comprehensibility. Seed weight was one of the main descriptors used to explain the origin and diversity of the species. The results found will be useful for studies that involve the conservation of genetic resources, mainly for the maintenance of germplasm banks and in breeding programs. In addition, it is recommended to integrate machine learning algorithms in studies aimed at classifying lima bean.

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