Neural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approach
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
20/05/2014
20/05/2014
01/01/2005
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Resumo |
This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R > 0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied. |
Formato |
633-644 |
Identificador |
http://dx.doi.org/10.1002/cbdv.200590040 Chemistry & Biodiversity. Zurich: Verlag Helvetica Chimica Acta Ag, v. 2, n. 5, p. 633-644, 2005. 1612-1872 http://hdl.handle.net/11449/35036 10.1002/cbdv.200590040 WOS:000229553300003 |
Idioma(s) |
eng |
Publicador |
Verlag Helvetica Chimica Acta Ag |
Relação |
Chemistry & Biodiversity |
Direitos |
closedAccess |
Tipo |
info:eu-repo/semantics/article |