Classification accuracy of algorithms for blood chemistry data for three aquaculture-affected marine fish species.
Fish Physiol Biochem
; 35(4): 641-7, 2009 Nov.
Article
em En
| MEDLINE
| ID: mdl-19031001
ABSTRACT
The objective of this study was determination and discrimination of biochemical data among three aquaculture-affected marine fish species (sea bass, Dicentrarchus labrax; sea bream, Sparus aurata L., and mullet, Mugil spp.) based on machine-learning methods. The approach relying on machine-learning methods gives more usable classification solutions and provides better insight into the collected data. So far, these new methods have been applied to the problem of discrimination of blood chemistry data with respect to season and feed of a single species. This is the first time these classification algorithms have been used as a framework for rapid differentiation among three fish species. Among the machine-learning methods used, decision trees provided the clearest model, which correctly classified 210 samples or 85.71%, and incorrectly classified 35 samples or 14.29% and clearly identified three investigated species from their biochemical traits.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise Química do Sangue
/
Algoritmos
/
Aquicultura
/
Peixes
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article