Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models
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Data
2012-12-01
Autores
Faridi, A.
Sakomura, N. K. [UNESP]
Golian, A.
Marcato, S. M.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Poultry Science Assoc Inc
Resumo
As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.
Descrição
Palavras-chave
support vector regression, Carcass characteristics, neural network
Como citar
Poultry Science. Savoy: Poultry Science Assoc Inc, v. 91, n. 12, p. 3286-3294, 2012.